Proceedings of the International AAAI Conference on Web and Social Media
The proceedings of the International AAAI Conference on Web and Social Media (ICWSM) provides an archival record of the ICWSM conference — a forum where researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.
Association for the Advancement of Artificial Intelligence
A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models
Automatically annotating job data with standardized occupations from taxonomies, known as occupation classification, is crucial for labor market analysis. However, this task is often hindered by data scarcity and the challenges of manual annotations. While large language models (LLMs) hold promise due to their extensive world knowledge and in-context learning capabilities, their effectiveness depends on their knowledge of occupational taxonomies, which remains unclear. In this study, we assess the ability of LLMs to generate precise taxonomic entities from taxonomy, highlighting their limitations, especially for smaller models. To address these challenges, we propose a multi-stage framework consisting of inference, retrieval, and reranking stages, which integrates taxonomy-guided reasoning examples to enhance performance by aligning outputs with taxonomic knowledge. Evaluations on a large-scale dataset show that our framework not only enhances occupation and skill classification tasks, but also provides a cost-effective alternative to frontier models like GPT-4o, significantly reducing computational costs while maintaining strong performance. This makes it a practical and scalable solution for occupation classification and related tasks across LLMs.
Copyright (c) 2026 Association for the Advancement of Artificial Intelligence
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
Large language models (LLMs) are starting to complement traditional information seeking mechanisms such as web search. LLM-powered chatbots like ChatGPT are gaining prominence among the general public. AI chatbots are also increasingly producing content on social media platforms. However, LLMs are also prone to hallucinations, generating plausible yet factually incorrect or fabricated information. This becomes a critical problem when laypeople start seeking information about sensitive issues such as healthcare. Existing works in LLM hallucinations in the medical domain mainly focus on testing the medical knowledge of LLMs through standardized medical exam questions which are often well-defined and clear-cut with definitive answers. However, these approaches may not fully capture how these LLMs perform during real-world interactions with patients. This work conducts a pioneering study on hallucinations in LLM-generated responses to real-world healthcare queries from patients. We introduce MEDHALU, a novel medical hallucination benchmark featuring diverse health-related topics and hallucinated responses from LLMs, with detailed annotation of the hallucination types and text spans. We also propose MEDHALUDETECT, a comprehensive framework for evaluating LLMs’ abilities to detect hallucinations. Furthermore, we study the vulnerability to medical hallucinations among three groups — medical experts, LLMs, and laypeople. Notably, LLMs significantly underperform human experts and, in some cases, even laypeople in detecting medical hallucinations. To improve hallucination detection, we propose an expert-in-the-loop approach that integrates expert reasoning into LLM inputs, significantly improving hallucination detection for all LLMs, including a 6.3% macro-F1 improvement for GPT-4.
AdFL: In-Browser Federated Learning for Online Advertisement
Since most countries are coming up with online privacy regulations, such as GDPR in the EU, online publishers need to find a balance between revenue from targeted advertisement and user privacy. One way to be able to still show targeted ads, based on user personal and behavioral information, is to employ Federated Learning (FL), which performs distributed learning across users without sharing user raw data with other stakeholders in the publishing ecosystem. This paper presents AdFL, an FL framework that works in the browsers to learn user ad preferences. These preferences are aggregated in a global FL model, which is then used in the browsers to show more relevant ads to users. AdFL can work with any model that uses features available in the browser such as ad viewability, ad click-through, user dwell time on pages, and page content. The AdFL server runs at the publisher and coordinates the learning process for the users who browse pages on the publisher's website. The AdFL prototype does not require the client to install any software, as it is built utilizing standard APIs available on most modern browsers. We built a proof-of-concept model for ad viewability prediction that runs on top of AdFL. We tested AdFL and the model with two non-overlapping datasets from a website with 40K visitors per day. The experiments demonstrate AdFL's feasibility to capture the training information in the browser in a few milliseconds, show that the ad viewability prediction achieves up to 92.59% AUC, and indicate that utilizing differential privacy (DP) to safeguard local model parameters yields adequate performance, with only modest declines in comparison to the non-DP variant.
The Gray Area: Characterizing Moderator Disagreement on Reddit
Volunteer moderators play a crucial role in sustaining online dialogue, but they often disagree about what should or should not be allowed. In this paper, we study the complexity of content moderation with a focus on disagreements between moderators, which we term the “gray area” of moderation. Leveraging 5 years and 4.3 million moderation log entries from 24 subreddits of different topics and sizes, we characterize how gray area, or disputed cases, differ from undisputed cases. We show that one-in-seven moderation cases are disputed among moderators, often addressing transgressions where users' intent is not directly legible, such as in trolling and brigading, as well as tensions around community governance. This is concerning, as almost half of all gray area cases involved automated moderation decisions. Through extensive empirical analyses, we show that even state-of-the-art language models struggle to adjudicate gray area cases. Focusing on improving these models is unpromising. Through information-theoretic evaluations, we demonstrate that gray area cases are inherently harder to adjudicate than undisputed cases. We highlight the key role of expert human moderators in overseeing the moderation process and provide insights about the challenges of current moderation processes and tools.
Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations
In emotionally supportive conversations, well-intended positivity can sometimes misfire, leading to responses that feel dismissive, minimizing, or unrealistically optimistic. We examine this phenomenon of incongruent positivity as miscalibrated expressions of positive support in both human and LLM generated responses. To this end, we collected real user–assistant dialogues from Reddit across a range of emotional intensities and generated additional responses using large language models for the same context. We categorize these conversations by intensity into two levels of Mild which covers relationship tension and general advice and Severe which covers grief and anxiety conversations. This level of categorization enables a comparative analysis of how supportive responses vary across lower and higher stakes contexts. Our analysis reveals that LLMs are more prone to unrealistic positivity through dismissive and minimizing tone, particularly in high stakes contexts. To further study the underlying dimensions of this phenomenon, we fine-tune LLMs on datasets with strong and weak emotional reactions. Moreover, we developed a weakly supervised multilabel classifier ensemble (DeBERTa and MentalBERT) that shows improved detection of incongruent positivity types across two sorts of concerns (Mild and Severe). Our findings shed light on the need to move beyond merely generating generic positive responses and instead study the congruent support measures to balance positive affect with emotional acknowledgment. This approach offers insights into aligning large language models with affective expectations in the online supportive dialogue, paving the way toward context-aware and trust-preserving online conversation systems.
Gender Disparities in StackOverflow’s Community-Based Question Answering: A Matter of Quantity Versus Quality
Community Question&Answering (CQA) platforms, such as Stack Overflow (SO), are valuable knowledge exchange and problem-solving resources. These platforms incorporate mechanisms to assess the quality of answers and participants' expertise, ideally free from discriminatory biases. However, prior research has highlighted persistent gender biases, raising concerns about the inclusivity and fairness of these systems. Addressing such biases is crucial for fostering equitable online communities. While previous studies focus on detecting gender bias by comparing male and female user characteristics, they often overlook the interaction between genders, inherent answer quality, and the selection of "best answers'' by question askers. In this study, we investigate whether answer quality is influenced by gender using a combination of human evaluations and automated assessments powered by Large Language Models. Our findings reveal no significant gender differences in answer quality, nor any substantial influence of gender bias on the selection of "best answers." Instead, we find that the significant gender disparities in SO's reputation scores are primarily attributable to differences in users' activity levels, e.g., the number of questions and answers they write. Our results have important implications for the design of scoring systems in CQA platforms. In particular, reputation systems that heavily emphasize activity volume risk amplifying gender disparities that do not reflect actual differences in answer quality, calling for more equitable design strategies.
Characterizing Online Activities Contributing to Suicide Mortality Among Youth
The recent rise in youth suicide highlights the urgent need to understand how online experiences contribute to this public health issue. Our mixed-methods approach responds to this challenge by developing a set of themes focused on risk factors for suicide mortality in online spaces among youth ages 10-24, and a framework to model these themes at scale. Using 29,124 open text summaries of death investigations between 2013-2022, we conducted a thematic analysis to identify 12 types of online activities that were considered by investigators or next of kin to be relevant in contextualizing a given suicide death. We then developed a zero-shot learning framework to model these 12 themes at scale, and analyzed variation in these themes by decedent characteristics and over time. Our work uncovers several online activities related to harm to self, harm to others, interpersonal interactions, activity levels online, and life events, which correspond to different phases of suicide risk from two prominent suicide theories. We find an association between these themes and decedent characteristics like age, means of death, and interpersonal problems, and many themes became more prevalent during the 2020 COVID-19 lockdowns. While digital spaces have taken some steps to address expressions of suicidality online, our work illustrates the opportunities for developing interventions related to less explicit indicators of suicide risk by combining suicide theories with computational research.
WildLLM: Uncovering Hidden Wildlife Trafficking on Social Media with Augmented and Fine-Tuned LLMs
Identifying potential wildlife trafficking posts on social media is challenging due to severe class imbalance, subtle linguistic variation, and the deliberate use of euphemisms. Previous work introduced a benchmark dataset that evaluated text-based models, such as BERT and RoBERTa. Still, their approach was limited by a highly imbalanced class dataset, depended on smaller pretrained models, and lacked augmentation strategies to capture neutral terms and disguised trade intent. These limitations leave open the question of whether large language models (LLMs), when augmented and fine-tuned using parameter efficient methods, can offer stronger performance in wildlife product trading (WLT) post detection. To fill the gaps, this paper proposes a text-based framework called WildLLM that utilizes LLMs with parameter efficient fine-tuning to detect WLT related posts on social media (e.g., Twitter/X), with a particular focus on ivory as a case study. Our framework, WildLLM, comprises three core strategies: (1) WLT focused data augmentation via multiple LLMs to alleviate class imbalance and enhance diversity; (2) improving the realism of augmented data through in context learning and prompt engineering; and (3) fine-tuning two LLMs (LLaMA-3.1-8B and Qwen2.5-7B) using Low Rank Adaptation (LoRA) with optimized hyperparameters. In our experiments, the proposed approach consistently outperformed five baselines, achieving 0.854 MCC and 0.927 Macro F1 with a 21% improvement in MCC and an 8.7% improvement in Macro F1 over the best performing baseline. These results were consistent across both Llama and Qwen backbone models, demonstrating the WildLLM's generalization capability regardless of the backbone LLM used. The results indicate that integrating parameter efficient fine-tuning with meticulously crafted augmentation produces a more effective text-based model for identifying WLT related posts online.
Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms
Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms and rules. But can LLMs apply community rules well enough to follow these norms? We evaluate LLMs’ capacity to detect (Task 1) and correct (Task 2) biased Wikipedia edits according to Wikipedia’s Neutral Point of View (NPOV) policy. LLMs struggled with bias detection, achieving only 64% accuracy on a balanced dataset. Models exhibited contrasting biases (some under- and others over-predicted bias), suggesting distinct priors about neutrality. LLMs performed better at bias correction, removing 79% of words removed by Wikipedia editors. However, LLMs made additional changes beyond Wikipedia editors’ simpler neutralizations, resulting in high-recall but low-precision editing. Interestingly, crowd-workers rated AI rewrites as more neutral (70%) and fluent- sounding (61%) than Wikipedia-editor rewrites. Qualitative analysis found that LLMs sometimes applied NPOV more comprehensively than Wikipedia editors but often made extraneous non-NPOV-related changes (e.g., grammar). LLMs may apply rules in ways that resonate with the public, but diverge from community experts. While potentially effective for generation, LLMs may reduce editor agency and increase moderation workload (e.g., verifying additions). Even when rules are easy to articulate, having LLMs apply them like community members may still be difficult.
Effects of Mainstream Visibility on Conspiracy Communities: Reddit After Epstein’s ‘Suicide’
Following the death of Jeffrey Epstein, the subreddit r/conspiracy experienced a significant visibility shock that brought mainstream users into direct contact with established conspiracy narratives. In this work, we explore how large-scale surges in public attention reshape participation and discourse within online conspiracy communities. We ask whether a sudden increase in exposure changes who join r/conspiracy, how long they stay, and how they adapt linguistically, compared with users who arrive through organic discovery. Using a computational framework that combines toxicity scores, survival analysis, and lexical and semantic measures over a period of 12 months, we find that mainstream visibility functions as a selection mechanism rather than a simple amplifier. Users who discover the conspiracy community through more organic pathways tend to integrate more quickly into its linguistic and thematic norms and show more stable engagement over time. By contrast, users who arrive during the height of public visibility remain semantically distant from core discourse and participate more briefly. Overall, we find that mainstream visibility is connected with changes in audience size, community composition, and linguistic cohesion. However, incidental exposure during attention shocks does not typically produce durable, integrated community members. These results provide a more nuanced understanding of how external events and platform visibility influence the growth and evolution of conspiracy spaces, offering insights for the design of responsible and transparent recommendation systems.
A Multidimensional Computational Analysis of Dehumanization in Incel Discourse
Dehumanizing language is a core feature of hostile online communities, often serving to reinforce ideology and justify harm. Despite widespread claims that incel discourse uniquely dehumanizes women, it remains unclear whether dehumanization systematically differs across gendered targets. This study provides the first theory-informed, multidimensional analysis of gendered dehumanization in incel discourse, operationalizing five dimensions (negative evaluation, moral disgust, animalistic framing, mind denial, and agency denial) and applying them to 10.3 million posts from incel forums (2018–2024). We extend an existing framework by adding a mind denial dimension and refining the operationalization of animality and negative evaluation, thereby strengthening its theoretical grounding and empirical coverage. Across analyses, women-associated terms demonstrate a small but statistically significant overall increase in dehumanization. However, none of the individual dimensions show reliable women–men differences, and these patterns remain stable over time. Taken together, the results indicate that dehumanization functions as an enduring representational baseline in this community: both women- and men-associated terms are evaluated negatively and embedded in dehumanizing contexts. These findings illustrate how theory-informed computational measures can characterize the multidimensional ways ideological harm is expressed in online communities.
Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Recommendation systems have traditionally relied on short-term engagement signals, such as clicks and likes, to personalize content. However, these signals are often noisy, sparse, and insufficient for capturing whether a recommendation supports future return to the platform. We introduce Retentive Relevance, a novel content-level survey-based feedback measure that directly assesses users’ intent to return to the platform for similar content. Unlike other survey measures that focus on immediate satisfaction, Retentive Relevance targets forward-looking behavioral intentions and provides a stronger predictor of next-day retention. We validate Retentive Relevance using psychometric analyses suited to our single-item measures, establishing convergent, discriminant, and behavioral validity. Through large-scale offline modeling, we show that Retentive Relevance significantly outperforms both engagement signals and other survey measures in predicting next-day retention, especially for users with limited historical engagement. We develop a production-ready proxy model that integrates Retentive Relevance into the final stage of a multi-stage ranking system on a social media platform. Calibrated score adjustments based on this model yield improvements in engagement, retention, and content quality during a 14-day A/B experiment. This work links content-level user perceptions to short-horizon retention outcomes in production systems. We offer a scalable, user-centered approach with implications for responsible AI development.
The Effect of Education in Prompt Engineering: Evidence from Journalists
Large language models (LLMs) are increasingly used to create content for social media, specifically in the context of journalism. In this paper, we analyze whether training in prompt engineering can improve the interactions of users with LLMs. For this, we conducted an experiment where we asked journalists to write short texts before and after training in prompt engineering. We then analyzed the effect of training on three dimensions: (1) the user experience of journalists when interacting with LLMs, (2) the domain expert perception, and (3) the non-expert reader perception, such as clarity, engagement, and other text quality dimensions. Our results show: (1) Our training improved the perceived expertise of journalists but also decreased the perceived helpfulness of LLM use. (2) The effect on expert perception varied by the difficulty of the task. (3) There is a mixed impact of training on reader perception across different text quality dimensions.
Why Conspiracy Theory Communities Endure: The Interplay of Feedback, Habits, and Community Context
Online conspiracy theory (CT) communities pose challenges for digital governance, yet the behavioral mechanisms sustaining their participation remain unclear. We analyze 2.1 million posts from 2,711 users across 4,435 subverses on Voat, an alternative platform with minimal moderation. Using survival analysis and hierarchical mixed-effects models, we show that habitual posting reduces sensitivity to social feedback, decoupling participation from the upvotes and downvotes that users receive. The type of content that users contribute varies in its effect: while group-oriented CT signals attenuate community engagement, trait-oriented signals, such as allegations of secrecy, have little effect. While posting habit is associated with disengagement from subverses in general, ideological alignment mitigates this effect in CT subverses. Together, these results reveal a multi-scale framework in which social reinforcement, routine participation, and community context interact to sustain CT engagement. By isolating mechanisms beyond CT alignment, we explain how fringe communities persist despite volatile rewards, providing new insights into the structural resilience of CT spaces.
POLAR: A Per-User Association Test in Embedding Space
Most intrinsic association probes operate at the word, sentence, or corpus level, obscuring author-level variation. We present COMPASS (COsine-Based Measure of Per-User Association with Semantic Subspaces), a per-user lexical association test that runs in the embedding space of a lightly adapted masked language model. Authors are represented by private deterministic tokens; COMPASS projects these vectors onto curated lexical axes and reports standardized effects with permutation p-values and Benjamini--Hochberg control. On a balanced bot--human Twitter benchmark, COMPASS cleanly separates LLM-driven bots from organic accounts; on an extremist forum, it quantifies strong alignment with slur lexicons and reveals rightward drift over time. The method is modular to new attribute sets and provides concise, per-author diagnostics for computational social science.
Tied In on TikTok: Tie Strength and Emotional Dynamics in Algorithmic Communities
Whether genuine communities can form on algorithmically-driven short-form video platforms like TikTok remains an open question, given that user interactions are often brief, dispersed, and difficult to trace. Building on theories of tie strength and online community formation, we examine whether eating disorder (ED) discourse on TikTok exhibits behavioral and emotional signatures of strong ties, including more frequent, reciprocal, and affectively intense interactions. In this paper, we analyze 43,040 ED-related TikTok videos and over 560,000 comments, alongside a Non-ED comparison dataset. We find that at the user-pair level, greater interaction frequency is associated with increasingly positive emotional expression, a pattern that is amplified in ED-related conversations. This trend is also reflected linguistically, with pairs that interact more frequently exhibiting more of a positive tone. At the same time, how a relationship starts matters: pairs that begin with positive exchanges usually stay mostly positive as they continue interacting, while pairs that begin negatively may add some positive exchanges over time but rarely become mostly positive. To contextualize these dynamics, we classify ED videos into three content types (Pro-Recovery, Pro-ED, and ED Experiences) and find that each exhibits distinct emotional interaction patterns. These findings suggest that dense, emotionally structured relationships can emerge within ED discourse on TikTok. More broadly, our work provides one of the first empirical demonstrations of how community-like relational dynamics form and persist on algorithmically driven short-form video platforms.
SpectrumNet: Detecting LGBTQ+ Cyberbullying with Dynamic Context-Aware Attention
Cyberbullying remains a critical societal issue, with LGBTQ+ individuals disproportionately affected. Although previous work proposed general cyberbullying detection models, LGBTQ+-targeted cyberbullying detection remains relatively unexplored. SpectrumNet, a novel transformer-based model introduced in this paper, goes beyond conventional cyberbullying detection by adding conversational context and identity-aware modeling. SpectrumNet freezes the RoBERTa backbone and adds three key components: a hierarchical attention network to capture linguistic nuance, a GRU-based encoder to better capture comment history, and a dynamic fusion module to effectively weigh contextual signals. To address dataset imbalance, we apply focal loss and weighted sampling. Trained on a large, annotated Instagram dataset, SpectrumNet effectively differentiates between non-bullying, general bullying, and LGBTQ+-targeted bullying. In particular, it achieves strong recall on targeted content and excels at detecting subtle forms of discrimination often missed in isolation but evident within threaded interactions.
From Attention to Dialogue: Does Audience Engagement Reinforce Constructive Cross-Party Communication?
While existing works have emphasized how elites shape mass opinion, we ask whether the reverse also holds: do audience reactions on social media actively shape elite behavior? We examine this question through the lens of cross-partisan interactions (CPIs), which can either foster deliberation or deepen polarization. Using a dataset of over 1.1 million cross-party retweets, replies, and mentions between U.S. state legislators and their audiences on Twitter/X (2020–2021), we first establish baseline patterns of engagement: Democrats gain modest engagement in replies and mentions, while Republicans often face penalties in direct cross-party interactions. Building on this, we show that audience engagement produces a feedback loop that conditions future elite behavior. Following highly visible CPIs, legislators are not only more likely to engage again in cross-talk, but also shift their rhetorical strategies. Engagement consistently promotes causal reasoning, subjective language, and positive-emotion framing in subsequent CPIs. These findings suggest a positive association between audience engagement and constructive cross-party discourse among elites, challenging overly simplified interpretations in the literature that emphasize social media as a primary driver of rising or falling polarization.
Self-moderation in the Decentralized Era: Decoding Blocking Behavior on Bluesky
Centralized social media platforms combine top-down moderation with user-level mechanisms like blocking, but access to behavioral and moderation-related data is typically restricted. In contrast, emerging decentralized networks like Bluesky provide researchers with open access to blocking actions, offering a unique opportunity to study self-moderation at scale. Understanding blocking behavior is critical in order to assess how community safety and user autonomy can be balanced in these environments. This study examines blocking on Bluesky, analyzing more than 100M actions by nearly 2M users over three months. We construct behavioral profiles from 86 features capturing user activity, content, and network interactions, and address two questions: (1) Can a user’s propensity to be blocked by others be estimated from in-platform behavior? and (2) Which behavioral features are most informative in predicting this propensity? Using state-of-the-art machine learning models, we achieve high accuracy in binary classification (AUROC > 0.85) and reliable performance in regression (R^2 ≈ 0.5). Our findings show that a small subset of features is sufficient for robust prediction, even when accounting for higher activity on the platform. Through explainability analyses, we identify the behavioral signals most strongly associated with blocking outcomes, enabling a transparent understanding of why certain users are more likely to be blocked. Our study provides a framework and empirical evidence that advances understanding of both the mechanisms and feature signals underlying user self-moderation, while also offering insights relevant to moderation design.
Triangulating Temporal Dynamics in Multilingual Swiss Online News
Analyzing news coverage in multilingual societies can offer valuable insights into the dynamics of public discourse and the development of collective narratives, yet comprehensive studies that account for linguistic and cultural diversity within national media ecosystems remain limited, particularly in complex contexts such as Switzerland. This paper studies temporal trends in Swiss digital media across the country's three main linguistic regions, French, German, and Italian, using a triangulated methodology that combines quantitative analyses with qualitative insights. We collected and processed over 1.7 million news articles (January 2019–January 2023), applying lexical metrics (diversity, density, sentence length), named entity recognition and Wikidata-based linking, targeted sentiment analysis, and consensus-based change-point detection. To enable principled cross-language comparisons and to connect to theories of domestication and cultural proximity, we derive domestication profiles together with a proximity salience ratio. Our analysis spans thematic, recurrent, and singular events (e.g. the British Royal Family, Christmas, Brexit, and the Swiss Wolf). By integrating quantitative data with qualitative interpretation, we provide new insights into the dynamics of Swiss digital media and demonstrate the usefulness of triangulation in media studies. The findings reveal distinct temporal patterns and highlight how linguistic and cultural contexts influence reporting. For example, French outlets emphasize France’s role in Brexit negotiations, while German and Italian media focused on broader European implications, patterns reflected in our domestication and proximity indices. In contrast, coverage of the Swiss Wolf is strongly localized, with each linguistic region highlighting its own cantons and concerns, and with consistently high Swiss anchoring across languages. Our approach offers a framework applicable to other multilingual or culturally diverse media environments, contributing to a deeper understanding of how news is shaped by linguistic and cultural factors.
Politics of Questions in News: A Mixed-Methods Study of Interrogative Stances as Markers of Voice and Power
Interrogatives in news discourse have been examined in linguistics and conversation analysis, but mostly in broadcast interviews and relatively small, often English-language corpora, while large-scale computational studies of news rarely distinguish interrogatives from declaratives or differentiate their functions. This paper brings these strands together through a mixed-methods study of the "Politics of Questions" in contemporary French-language digital news. Using over one million articles published between January 2023 and June 2024, we automatically detect interrogative stances, approximate their functional types, and locate textual answers when present, linking these quantitative measures to a qualitatively annotated subcorpus grounded in semantic and pragmatic theories of questions. Interrogatives are sparse but systematically patterned: they mainly introduce or organize issues, with most remaining cases being information-seeking or echo-like, while explicitly leading or tag questions are rare. Although their density and mix vary across outlets and topics, our heuristic suggests that questions are overwhelmingly taken up within the same article and usually linked to a subsequent answer-like span, most often in the journalist’s narrative voice and less often through quoted speech. Interrogative contexts are densely populated with named individuals, organizations, and places, whereas publics and broad social groups are mentioned much less frequently, suggesting that interrogative discourse tends to foreground already prominent actors and places and thus exhibits strong personalization. We show how interrogative stance, textual uptake, and voice can be operationalized at corpus scale, and argue that combining computational methods with pragmatic and sociological perspectives can help account for how questioning practices structure contemporary news discourse.
Posts of Peril: Detecting Information About Hazards in Text
Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media. However, an indicator that is often overlooked is the presence or absence of information concerning harms or hazards. Detecting such indicators in text is important because substantial research demonstrates that negative events are more likely to be attended to, and more likely to elicit a response. In addition, statements about hazards are often found to be more believable than statements about benefits. Here, we develop a new model to detect information concerning hazards, trained on a new collection of annotated X posts. We show that not only does this model perform well (outperforming, e.g., dictionary approaches), but that the hazard information it extracts is not strongly correlated with such widely used indicators as moral outrage, sentiment, and emotions. (That said, in accord with expectations, hazard information does correlate positively with such emotions as fear, and negatively with emotions like joy.) To demonstrate the utility of our tool, we apply it to two datasets of X posts that discuss important geopolitical events, namely the Israel-Hamas war and the 2022 French national election. In both cases, we find that hazard information, especially information concerning conflict, is common. We extract accounts associated with information campaigns from each data set to explore how information about hazards could be used to attempt to influence geopolitical events. We find that inorganic accounts representing the viewpoints of weaker sides in a conflict often discuss hazards to civilians, potentially as a way to elicit aid for the weaker side. Moreover, the rate at which these hazards are mentioned differs markedly from organic accounts, likely reflecting information operators' efforts to frame the given geopolitical event for strategic purposes. These results are first steps towards exploring hazards within an information warfare environment. The model is shared as a Python package to help researchers and journalists analyze hazard content.
Examining Algorithmic Curation on Social Media: An Empirical Audit of Reddit’s r/popular Feed
Platforms are increasingly relying on algorithms to curate the content within users' social media feeds. However, the growing prominence of proprietary, algorithmically curated feeds has concealed what factors influence the presentation of content on social media feeds and how that presentation affects user behavior. This lack of transparency can be detrimental to users, from reducing users' agency over their content consumption to the propagation of misinformation and toxic content. To uncover details about how these feeds operate and influence user behavior, we conduct an empirical audit of Reddit's algorithmically curated trending feed called r/popular. Using 10K r/popular posts collected by taking snapshots of the feed over 11 months, we find that recent comments help a post remain on r/popular longer and climb the feed. We also find that posts below rank 80 correspond to a sharp decline in activity compared to posts above. When examining the effects of having a higher proportion of undesired behavior---i.e., moderator-removed and toxic comments---we find no significant evidence that it helps posts stay on r/popular for longer. Although posts closer to the top receive more undesired comments, we find this increase to coincide with a broader increase in overall engagement---rather than indicating a disproportionate effect on undesired activity. The relationships between algorithmic rank and engagement highlight the extent to which algorithms employed by social media platforms essentially determine which content is prioritized and which is not. We conclude by discussing how content creators, consumers, and moderators on social media platforms can benefit from empirical audits aimed at improving transparency in algorithmically curated feeds.
Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies
The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts, remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of the channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by extreme users also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.
Flattening Fantasies: Analyzing Stereotypes in Pornographic Discourse
This study presents a large-scale quantitative analysis of 5.4 million video titles from xHamster, the world's fourth most visited pornography platform, to examine linguistic patterns used to describe different dimensions of identities (e.g., race and gender). Specifically, we investigate how nationalities, ethnic groups, and gender identities are portrayed in pornographic discourse, focusing on stereotypes and deviations from implicit norms of pornography. The results show the dominance of American pornography over other groups, which acts as the defacto norm for the adult industry. We identify persistent ethnic stereotypes reflecting colonial power dynamics, religious fetishization, and the sexualization of cultural taboos. The analysis reveals gendered representational differences across cultures, with a prevalent Madonna-Whore dichotomy in the portrayal of women and stereotypical depictions of men in physically dominant roles.
The Meme Is the Message: Generative Memesis and AI Visuals in the 2024 USA Presidential Elections
Visual content on social media has become increasingly influential in shaping political discourse and civic engagement, but it also limits participation due to the increased cost of multimedia production. In tandem, the growth of generative AI provides novel ways for citizens to participate in politics by lowering these costs. Drawing on a dataset of 239,526 Instagram images, we analyze the effects of synthetic images during the 2024 United States presidential election, using a multimodal workflow combining computer vision, large language models, and facial affect analysis. Results show that meme format is a stronger predictor of engagement than AI-generated content alone. However, AI-generated memes yield a significant interaction effect, suggesting synergistic increases in engagement when synthetic imagery is integrated with memes through human curation. We also characterize how users curate images. Partisans use AI in different ways: Democrat-leaning users tend to use it for in-group support, whereas Republican-leaning users more often employ it for out-group attacks. Users generally select happier synthetic faces compared to real photographs. We define generative memesis as a mode of communication in which memes are no longer shared person-to-person, but mediated by AI through customized visuals. We discuss how generative AI may empower civic participation, the bifurcation of content production and curation, and its implications for in the history of novel technologies and participatory culture.
Decoding Influence Narratives: Identifying Persuasion and Propaganda Techniques in Twitter Discourse on Violent Extremism
Identifying persuasion and propaganda techniques is crucial for understanding message agendas and designing culturally resonant counter-messages, yet their overlapping nature complicates both qualitative annotation and automated detection efforts. Previous research often examined persuasion and propaganda separately, overlooking their frequent convergence in everyday communication and in digital contexts. To address this gap, our study developed a codebook of 20 unique influence techniques by integrating operationalisations from prior analyses of radical Islamic publications with insights from tweets discussing the 2020 Charlie Hebdo–related terror incidents. As techniques often co-occur, we clustered them to uncover underlying intent and context, distilling the 20 techniques into seven overarching strategies, four of which centred on authority and fallacy-based invocations. A cross-cultural analysis of tweets from five Asian Islamic subgroups then examined differences in susceptibility to these strategies, moving beyond the supply of extremist messaging to reveal the demand-side dynamics of what resonated with audiences. Results revealed significant variation: the Arabic subgroup, for instance, was more susceptible to religious appeals and misinformation-related fallacies than other subgroups. These findings demonstrate that influence strategies are not universally applicable, underscoring the importance of tailoring counter-propaganda messaging to the cultural values of specific audiences.
The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube
The digital transformation of religious practice has reshaped how billions of people engage with spiritual content, with video-sharing platforms becoming central to contemporary religious communication. Yet current research lacks systematic understanding of how narrative and visual elements create meaningful spiritual experiences and foster viewer engagement. We present a mixed-methods study of popular religious videos on YouTube across major religions, developing taxonomies of narrative frameworks, visual elements, and viewer interaction. Using LLM-assisted analysis, we studied relationships between content characteristics and viewer responses. Findings shows religious videos predominantly adopt speaking-style formats with authority-based persuasion strategies, using salvation narratives for guidance. All prefer bright lighting, with Buddhism favoring warm tones and prominent symbols, Judaism preferring indoor settings, and Hinduism emphasizing sacred objects. We identified differentiated patterns of emotional sharing among religious viewers while revealing significant correlations between content characteristics and engagement, particularly regarding AI-generated content.
Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case Study
This study explores computational approaches for measuring moral foundations (MFs) in non-English corpora. Since most resources are developed primarily for English, cross- linguistic applications of moral foundation theory remain limited. Using Chinese as a case study, this paper evaluates the effectiveness of applying English resources to machine translated text, local language lexicons, multilingual encoder-only language models, and decoder-only large language models (LLMs) in measuring MFs in non-English texts. The results indicate that machine translation and local lexicon approaches are insufficient for complex moral assessments, frequently resulting in a substantial loss of cultural information. In contrast, language models demonstrate reliable cross-language performance with transfer learning, with LLMs excelling in terms of data efficiency. Importantly, this study also underscores the need for human-in-the-loop validation of automated MF assessment, as even the most advanced models may overlook cultural nuances and face potential risks in cultural misalignment. The findings highlight the potential of LLMs for cross-language MF measurements and other complex multilingual deductive coding tasks.
LaMSUM: Amplifying Voices Against Harassment Through LLM Guided Extractive Summarization of User Incident Reports
Citizen reporting platforms help the public and authorities stay informed about sexual harassment incidents. However, the high volume of data shared on these platforms makes reviewing each individual case challenging. Therefore, a summarization algorithm capable of processing and understanding various code-mixed languages is essential. In recent years, Large Language Models (LLMs) have shown exceptional performance in NLP tasks, including summarization. LLMs inherently produce abstractive summaries by paraphrasing the original text, while the generation of extractive summaries – selecting specific subsets from the original text – through LLMs remains largely unexplored. Moreover, LLMs have a limited context window size, restricting the amount of data that can be processed at once. We tackle these challenges by introducing LaMSUM, a novel multi-level framework combining summarization with different voting methods to generate extractive summaries for large collections of incident reports using LLMs. Extensive evaluation using four popular LLMs (Llama, Mistral, Claude and GPT-4o) demonstrates that LaMSUM outperforms state-of-the-art extractive summarization methods. Overall, this work represents one of the first attempts to achieve extractive summarization through LLMs, and is likely to support stakeholders by offering a comprehensive overview and enabling them to develop effective policies to minimize incidents of unwarranted harassment.
Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility
Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reproduce patterns of misinformation susceptibility remains unclear. We evaluate whether LLM-simulated survey respondents replicate human patterns of misinformation belief and sharing. Using participant profiles from three online surveys that include network, demographic, attitudinal, and behavioral features, we prompt LLMs to simulate survey responses to misinformation items and compare the results to human data on distributions and associations. LLM simulations capture broad distributional tendencies and show modest correlation with human responses. However, they systematically overstate the association between belief and sharing. Linear models fitted to simulated responses show substantially inflated explained variance compared to those fitted to human data. They also place disproportionate weight on attitudinal and behavioral features while largely ignoring personal network characteristics, a pattern not observed in human data. Analyses of LLM training data and model-generated reasoning paths suggest that these distortions reflect systematic biases in how misinformation-related concepts are represented. Our findings suggest that LLM-based survey simulations are better suited for diagnosing systematic deviations from human judgment than for substituting for it.
NewsDB: An Automated Approach to Build an Extensive Database of Self-Proclaimed News Providers
The credibility of news obtained online has become a concern due to the ease with which individuals or groups can claim to be news publishers and share news-related content. Unfortunately, research on monitoring misleading information in the online news ecosystem is hindered because the community lacks a comprehensive and up-to-date list of social media pages and domains claiming to be news media. This paper employs an automated approach that uses Google's GNews API and Meta's CrowdTangle API to identify self-proclaimed news providers. Our method was able to discover 19k self-proclaimed news providers in the United States active in June 2022 and 23k active in October 2020. Additionally, we retrieve the posting history (totaling 191,182,320 posts) of discovered pages. Among others, our analysis reveals that, on average, 300 new self-proclaimed news pages are created every four months, 56% of them do not declare a managing organization, 15% of the identified news pages are news aggregators, and 57% declare to be local news.
Political Advertising on Facebook During the 2022 Australian Federal Election: A Social Identity Perspective
Political advertising on social media has become an important component of contemporary election campaigns, yet most research has focused on voluntary voting systems in which mobilizing supporters is a central goal. This study examines political advertising on Meta platforms (Facebook and Instagram) during the 2022 Australian federal election, a context shaped by compulsory voting and therefore distinctive campaign incentives. Using data from Meta’s Ad Library, we analyze temporal, demographic, geographic, and textual patterns in advertisements published by political actors during the campaign. The Australian Labor Party and the Liberal Coalition dominated the digital advertising landscape in both spending and impressions, while smaller parties and independents adopted more selective strategies. Ad delivery varied across demographic groups, with major parties, the Greens, and the United Australia Party showing different age and gender skews, though the clearest form of targeting was geographic. Across content, major parties emphasized party branding and criticism of opponents, whereas smaller parties more often relied on issue-based messaging. Interpreted through political psychology perspectives, and particularly Social Identity Theory, these patterns are consistent with different forms of identity appeal: major parties appear to reinforce established partisan identities, while smaller actors seek to connect with voters through issue-based identities to capture the support of disaffected voters who are obligated to participate.
Simulating Online Social Media Conversations on Controversial Topics Using AI Agents Calibrated on Real-World Data
Online social networks offer a valuable lens to analyze both individual and collective phenomena. Researchers often use simulators to explore controlled scenarios, and the integration of Large Language Models (LLMs) makes these simulations more realistic by enabling agents to understand and generate natural language content. In this work, we offer an exploratory investigation of the behavior of LLM-based agents in a simulated microblogging social network. We initialize agents with realistic profiles and opinions calibrated on real-world online conversations from the 2022 Italian political election and extend an existing simulator by introducing mechanisms for opinion modeling. We examine how LLM agents simulate online conversations, interact with others, and evolve their opinions under different scenarios. Our results show that LLM agents generate coherent content, form connections, and build a realistic social network structure. However, their generated content displays less heterogeneity in tone and toxicity compared to real data. We also find that LLM-based opinion dynamics evolve over time in ways similar to traditional mathematical models. Varying parameter configurations produces no significant changes, indicating that simulations require more careful cognitive modeling at initialization to replicate human behavior more faithfully. Overall, we demonstrate the potential of LLMs for simulating user behavior in social environments, while also identifying key challenges in capturing heterogeneity and complex dynamics.
Detecting and Enhancing Intellectual Humility in Online Political Discourse
Intellectual humility (IH)—a recognition of one's own intellectual limitations—can reduce polarization and foster more understanding across lines of difference. Yet little work explores how IH can be systematically defined, measured, evaluated, and enhanced in spaces that often lack it the most: online political discussions. In this paper, we seek to bridge these gaps by exploring two questions: 1) how might pre-existing levels of IH influence future expressions of IH during online political discourse? and 2) can online interventions enhance IH across different political topics and conversational environments? To pursue these questions, we define a codebook characterizing different dimensions of IH and intellectual arrogance (IA) and have researchers use it to annotate several hundred Reddit posts, which we then use to develop and validate a classifier to support IH analysis at scale. These tools subsequently enable two key contributions: i) an observational data analysis of how IH varies across different political discussions on Reddit, which reveals that more/less IH environments tend to contain future posts of a similar nature, and ii) a randomized control trial evaluating strategies for nudging discussion participants to demonstrate more IH in their posts, which reveals the possibility of enhancing IH in online discussions across a range of contentious topics. Our findings highlight the possibility of measuring and increasing IH online without necessarily reducing engagement.
Jointly Modelling the Evolution of Social Structure and Language in Online Communities
Group interactions take place within a particular socio-temporal context, which should be taken into account when modelling interactions in online communities. We propose a method for jointly modelling community structure and language over time. Our system produces dynamic word and user representations that can be used to cluster users, investigate thematic interests of groups, and predict group membership. We apply and evaluate our method in the context of a set of misogynistic extremist groups. Our results indicate that this approach outperforms prior models which lacked one of these components (i.e. not incorporating social structure, or using static word embeddings) when evaluated on clustering and embedding prediction tasks. Our method further enables novel types of analyses on online groups, including tracing their response to temporal events and quantifying their propensity for using violent language, which is of particular importance in the context of extremist groups.
The Cross-Partisan Voices of Podcasts and Their Social Media Ties
Heightened polarization in the United States has raised public and scholarly concern about podcasts and their role in political division. Podcasts that repeatedly host cross-partisan discussions, however, remain understudied. In this work, we examine whether individuals who engage with politically heterogeneous voices on podcasts also display bridging behavior on social media and in their cross-cutting conversations. Specifically, we investigate whether bridging actors follow a more politically diverse set of accounts on social media and whether they discuss topics that differ from those of their non-bridging counterparts. To do so, we collect data on roughly 5,000 individuals who appeared on popular political podcasts over a 13-month period, including their podcast appearances and discussion content. We also identify their X accounts and extract the follower relationships among them. We find that podcasters and their guests interact with a smaller but more politically diverse set of individuals on podcasts than on X, except for liberal podcasts, which maintain similar cross-cutting ties across both media. To study cross-ideological conversation, we transcribed and diarized over 10,000 podcast episodes. We find that bridging guests tend to discuss a narrower set of topics during cross-cutting podcast interviews compared to non-bridging guests. These findings indicate that bridging actors in the podcasting space exhibit distinguishable conversational and attentional behaviors, laying the groundwork for further research examining these actors and their conversations across various media.
The Role of Follow Networks and Twitter’s Content Recommender on Partisan Skew and Rumor Exposure During the 2022 U.S. Midterm Election
Social media platforms shape users' experiences through the algorithmic systems they deploy. In this study, we examine to what extent Twitter's content recommender, in conjunction with a user's social network, impacts the topic, political skew, and reliability of information served on the platform during a high-stakes election. We utilize automated accounts to document Twitter's algorithmically curated and reverse chronological timelines throughout the U.S. 2022 midterm election. We find that the algorithmic timeline measurably influences exposure to election content, partisan skew, and the prevalence of low-quality information and election rumors. Critically, these impacts are mediated by the partisan makeup of one's personal social network, which often exerts greater influence than the algorithm alone. We find that the algorithmic feed decreases the proportion of election content shown to left-leaning accounts, and that it skews content toward right-leaning sources when compared to the reverse chronological feed. We additionally find evidence that the algorithmic system increases the prevalence of election-related rumors for right-leaning accounts, and has mixed effects on the prevalence of low-quality information sources. Our work provides insight into the outcomes of Twitter's complex recommender system at a crucial time period before controversial changes to the platform and in the midst of nationwide elections and highlights the need for ongoing study of algorithmic systems and their role in democratic processes.
A Large Scale Social Web Audit of AI Generated Text Detection Systems
This paper makes three contributions. First, we exploit temporal signals to conduct an in-the-wild audit of a broad suite of AI-generated text detection (AGTD) systems. Our in-the--wild audit reveals that state-of-the-art (SoTA) AGTD systems exhibit considerable false positives. Second, our audit demonstrates that AGTD systems disfavor liberal political discourse and flags them more often as AI-generated as compared to conservative political discourse. Finally, we extend anticontent sampling approach to robustify existing AGTD systems.
Rabble-Rousers in the New King’s Court: Algorithmic Effects on Account Visibility in Pre-X Twitter
Algorithmic effects on social media platforms have come under recent scrutiny, with several studies reporting that right-leaning accounts tend to receive more exposure. In this paper, we expand upon this body of work using data collected from user feeds after Twitter's change of ownership but before its re-branding to X. We replicate findings from prior work regarding the increased exposure of right-leaning accounts to wider audiences in algorithmically curated compared to reverse-chronological feeds, and, crucially, we further unpack this effect to illuminate what correlated (and did not correlate) with these differences. Our results reveal that right-leaning accounts benefited not necessarily due to their political affiliation, but likely because they behaved in ways associated with algorithmic rewards; namely, posting more agitating content and receiving attention from the platform's owner, Elon Musk, who was the most central network account. We also demonstrate that legacy-verified accounts, like businesses and government officials, received less exposure in the algorithmic feed compared to non-verified or Twitter Blue-verified accounts. We discuss implications of these findings for the intersection between behavioral incentives for algorithmic reach and the health of online discourse.
Information Pathways in Online Science Communication: The Role of Platform Actors and News Media
Online discussions of science involve complex interactions among experts, news media, and social media users as they interpret and disseminate scientific findings. While prior work has examined these actors in isolation, their interplay in shaping science communication remains poorly understood. Using the COVID-19 pandemic as a case study, we analyze 1.24M tweets and 211k news articles that reference pandemic-related scientific papers. We find that the most influential Twitter accounts in this discourse are predominantly individuals with medical or research credentials. However, we also identify a coordinated network that disproportionately amplifies a small set of prominent credentialed experts who advance contrarian, anti-consensus positions on vaccines, lockdowns, and related topics. The papers promoted by these influential actors substantially overlap with those covered by news media, but with key differences: pro-consensus experts primarily engage with studies featured by mainstream and medical outlets, whereas contrarian experts align more closely with papers promoted by low-quality, pseudoscientific, or conspiratorial sources. Notably, news outlets tend to report on scientific studies after they have been highlighted by social media superspreaders. Together, these findings reveal multi-level pathways of information flow and coordinated amplification structures that shape science communication across social media and news, offering new insights into the dynamics of the broader information ecosystem.
How Persuasive Are LLMs in the Wild? Assessing Personalized Ads in Real-World Delivery
Large language models (LLMs) have shown persuasive potential in controlled studies and surveys across commercial, political, and social domains; their effectiveness in real-world settings, however, remains largely underexplored. This work bridges that gap by evaluating LLM-generated personalized messages deployed through advertising campaigns on Meta platforms. We generate ads using three LLMs (GPT-4o, Gemini 1.5 Pro, and LLaMA 3.1), targeting four demographic groups through three distinct personalization strategies—adapting tone and language, introducing audience-relevant themes, and selectively emphasizing elements from the source material. We assess their performance across three dimensions: (1) user engagement through live testing on social media, (2) perceived appeal via user surveys, and (3) platform behavior through algorithmic delivery analysis. Our results show that personalized messages, deployed through Meta's ad delivery system, do not significantly improve user engagement compared to non-personalized alternatives, and for some demographic groups specific strategies reduced engagement further. We also find that survey-based assessments of ad appeal can diverge from observed behavioral outcomes, highlighting the limitations of relying solely on self-reported metrics to evaluate LLM-based personalization. Finally, we show that LLM-generated personalization cues can shift algorithmic ad delivery toward the intended audience by up to 8% without explicit targeting instructions, but that this influence is bounded by the platform's own relevance predictions. Together, these findings provide an empirical assessment of both the potential and the limits of LLM-based personalization within algorithmically mediated advertising systems.
Whose Values? Measuring the (Subjective) Expression of Basic Human Values in Social Media Posts
The value alignment of sociotechnical systems has become a central debate, but progress depends on how human values are perceived in the content these systems surface and how such perceptions can be measured at scale. Social media platforms are a prominent class of sociotechnical systems where algorithmic curation shapes exposure to value-laden content at scale. Large-language models offer new opportunities for measuring expressions of human values (e.g., humility or equality) in social media data, but value expressions can be subjective: different people will annotate the same post with different values. In this paper, we draw on the Schwartz value system as a broadly encompassing and theoretically grounded set of basic human values, and introduce a framework to personalize the measurement of expressions of Schwartz values in social media posts at scale. We collect 32,370 ground truth value expression annotations from N=1,079 people on 5,211 social media posts representative of real users' feeds. Due to the subjectivity of the task, we observe low levels of inter-rater agreement between people, and low agreement between human raters and LLM-based methods. In response, we construct a personalization architecture for classifying value expressions by learning from a small number of highly informative calibration annotations per user. In evaluation, we find that modeling these differences successfully yields value expression predictions that people agree with more than they agree with other people. These results contribute new methods and understanding for the measurement of human values in social media data.
Fabricating Holiness: Characterizing Religious Misinformation Circulators on Arabic Social Media
Misinformation is a growing concern in a decade involving critical global events. While social media regulation is mainly dedicated towards the detection and prevention of fake news and political misinformation, there is limited research about religious misinformation which has only been addressed through qualitative approaches. In this work, we study the spread of fabricated quotes (Hadith) that are claimed to belong to Prophet Muhammad (the prophet of Islam) as a case study demonstrating one of the most common religious misinformation forms on Arabic social media. We attempt through quantitative methods to understand the characteristics of social media users who interact with fabricated Hadith. We spotted users who frequently circulate fabricated Hadith and others who frequently debunk it to understand the main differences between the two groups. We used Logistic Regression to automatically predict their behaviors and analyzed its weights to gain insights about the characteristics and interests of each group. We find that both fabricated Hadith circulators and debunkers have generally a lot of ties to religious accounts. However, circulators are identified by many accounts that follow the Shia branch of Islam, Sunni Islamic public figures from the gulf countries, and many Sunni non-professional pages posting Islamic content. On the other hand, debunkers are identified by following academic Islamic scholars from multiple countries and by having more intellectual non-religious interests like charity, politics, and activism.
Online Opinion Conflict Interaction Recognition Based on Dependent Multi-Task Deep Learning
Expression of conflicting opinions often appear in the interaction process of different users in various online communities, and effectively recognizing opinion conflict interactions is of great significance. Compared to traditional online opinion mining tasks, such as stance/sentiment classification, the opinion conflict interaction recognition shows unique and challenging characteristics that it is determined based on the interaction between the differences in emotion expression and the consistency of thematic content given two opinions. Thus, the paper tries to propose a model that can effectively model its unique feature and recognize different interaction categories, that is, Opinion Conflict Interaction Recognition based on Causal Inference-enhanced dependent Multi-Task Deep Learning, noted as CIMDL. The model introduced casual inference-enhanced multi-task learning and co-attention interaction mechanism in addition to the pre-training language model-based text embedding and deep neural network-based feature extraction. We construct two benchmark datasets for the new proposed task, and conduct extensive experiments to demonstrate the advantages of the proposed approach over different state-of-arts baselines.
Contextualizing Recommendation Explanations with LLMs: A User Study
Large language models (LLMs) are increasingly used in recommender systems to generate natural-language explanations for contextualized recommendations. This study examines how different types of LLM-generated explanations shape user perceptions and behavioral intentions in the context of movie recommendations. In a between-subjects online experiment (N=759) and follow-up interviews (N=30), we compare LLM-generated (a) generic explanations and (b) contextualized explanations. We find that contextualized explanations better address users’ cognitive and affective needs and strengthen intention to watch the recommended movies. Interview findings further show that the effectiveness of contextualization depends on how well explanations align with users’ genre and narrative preferences. References to prior movies can improve user perceptions and intent, but excessive detail may make explanations feel redundant. The benefits of contextualization are pronounced among users with higher engagement and greater trust in the system. Together, these findings highlight the potential of LLMs to support more user-centered recommendation experiences and offer practical guidance for designing explanations that improve trust, relevance, and engagement in entertainment platforms.
Permutation-Based Testing of Topic Co-occurrence: A Network Analysis of Reddit Debates on DOGE, Tariffs, and the Big Beautiful Bill
We study how policy arguments are structured in Reddit discussions of three initiatives from the early period of Trump's second presidency: the Department of Government Efficiency, the Liberation Day Tariffs, and the Big Beautiful Bill. We identify recurring policy considerations using a dictionary-based approach that is validated through manual coding and applied to full-text submissions. To distinguish meaningful associations from chance, we use a permutation-based test that compares observed co-occurrence counts for each pair of considerations to counts from randomly shuffled data with the same overall frequencies. From the pairs that pass this test, we build networks for each policy and describe their main features, including central topics, clusters of related considerations, and bridge topics that link clusters. The results show that the three debates have different argument structures and illustrate a simple way to add statistical testing to topic co-occurrence analysis.
Personal Experience and Public Support for Internet Shutdowns in Rural India
India, despite being the world's largest democracy, leads the globe in the number of government-imposed internet shutdowns. While internet access is crucial for daily life, there is paradoxically growing public support for these shutdowns. This raises important questions about how individuals form their opinions on internet shutdowns and whether personal experience can alter these beliefs. In this paper, we develop and test an experiential intervention to examine whether personally experiencing an internet shutdown changes individuals' attitudes toward such policies. We recruited 246 participants from rural India and asked them to voluntarily disconnect from the internet for 48 hours. Our results reveal that contrary to expectations, participants did not show a significant shift in their beliefs. In fact, a slight increase in support for internet shutdowns was observed after the intervention. Qualitative feedback suggests that participants often substituted internet use with offline activities, such as spending time with family or focusing on personal development, which may have contributed to this unexpected outcome. This study highlights the difficulty of changing deeply held beliefs, even when individuals experience firsthand the inconvenience of an internet shutdown. Our findings emphasize the complexity of public opinion on digital rights and governance, suggesting that experiential interventions alone may not be sufficient to shift perspectives. The study contributes to the broader discourse on internet governance and the challenges of addressing public support for internet restrictions.
Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
Political discourse has grown increasingly fragmented across different social platforms, making it challenging to trace how narratives spread and evolve within such a fragmented information ecosystem. Reconstructing social graphs and information diffusion networks is challenging, and available strategies typically depend on platform-specific features and behavioral signals which are often incompatible across systems and increasingly restricted. To address these challenges, we present a platform-agnostic framework that allows to accurately and efficiently reconstruct the underlying social graph of users' cross-platform interactions, based on discovering latent narratives and users' participation therein. Our method achieves state-of-the-art performance in key network-based tasks: information operation detection, ideological stance prediction, and cross-platform engagement prediction—while requiring significantly less data than existing alternatives and capturing a broader set of users. When applied to cross-platform information dynamics between Truth Social and X (formerly Twitter), our framework reveals a small, mixed-platform group of bridge users, comprising just 0.33% of users and 2.14% of posts, who introduce nearly 70% of migrating narratives to the receiving platform. These findings offer a structural lens for anticipating how narratives traverse fragmented information ecosystems, with implications for cross-platform governance, content moderation, and policy interventions.
The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act
To facilitate accountability and transparency, the Digital Services Act (DSA) sets up a process through which Very Large Online Platforms (VLOPs) need to grant vetted researchers access to their internal data (Article 40(4)). Although expectations are high for this mechanism to provide unprecedented data access as part of the DSA compliance obligations, operationalising it is challenging for two reasons. First, data access is only available for research on systemic risks affecting European citizens, a concept that still raises high levels of legal uncertainty. Second, data access suffers from an inherent standoff problem. Researchers need to request specific data but are not in a position to know all internal data collected, processed and stored by VLOPs, who, in turn, expect and demand data specificity for potential access. To contribute to the discussion of how Article 40 can be interpreted and applied, we provide a concrete illustration of what data access can look like in a real-world systemic risk case study. Our goal is not only to share with the research community at large a scenario for reflection, but also to provide hands-on insights into what type of data platforms may be required to share through the DSA. To this end, we focus on the 2024 Romanian presidential election interference incident, as this event is the first of its kind to trigger systemic risk investigations by the European Commission. In the context of these elections, one candidate is said to have benefited from TikTok algorithmic amplification through a complex and multilayered dis- and misinformation campaign. By analysing this incident, we can concretely comprehend election-related systemic risk in order to explore practical research tasks and compare necessary data with available TikTok data. In particular, our study makes two contributions: (i) we combine insights from law, computer science and platform governance to shed light on the complexities of studying systemic risks in the context of election interference, focusing on two relevant factors: platform manipulation and hidden advertising; and (ii) we provide practical insights into various categories of available data for the study of TikTok, based on platform documentation, data donations and TikTok's Research API.
The Limits of De-Politicizing–and Also of Annotation: A Case Study in Russian Media Outlets’ Social Media Posts, 2016–2024
We seek to understand the impact of the February 2022 invasion of Ukraine on political coverage and engagement with it. Accordingly, we introduce new annotated data with more than 2 million social media posts from prominent Russian-language print and online media outlets. After manually annotating 6,661 posts that appeared on V'Kontakte (VK) as well as additional posts on Facebook and Telegram—and supplementing human annotation with Large Language Models (LLMs)—we assess the validity and reliability of our measures before considering substantive questions. Varied analyses point to one conclusion: the ecosystem for political news durably changed with the invasion. Post-invasion, political posts spiked, and social media users became more likely to engage with political posts versus non-political posts. Moreover, analyses using embeddings illustrate that the similarity between independent and government-oriented media posts briefly grew after the invasion. By analyzing both outlets' coverage and engagement with it, this research indicates that autocrats' strategies for managing news coverage are not static. Instead, autocratic regimes may abandon de-politicization in favor of more invasive approaches when events drive heightened engagement with news. This research also provides methodological tools for future research. It evaluates the value and limitations of deploying LLMs to annotate and analyze text in Russian, and also of translating text from Russian to English before annotation or analysis. This research contributes to our understanding of the possibilities and limits of using LLMs to measure more abstract concepts, too.
Invariant Subgraphs for Cross-Domain Fake News Detection via Causal Disentanglement
The spread of misinformation through social media poses significant threats. Recent models using text and graph features have shown promising results in specific misinformation detection scenarios. However, these data-driven models heavily rely on training data that share similar distribution with inference data, limiting their applicability to misinformation from emerging or previously unseen domains, known as out-of-distribution (OOD) data. Tackling OOD misinformation is a challenging yet critical task. To address the challenge, we propose the Causal Subgraph-oriented Domain Adaptive misinformation Detection (CSDA) model. Based on a causal analysis, CSDA extracts invariant substructures from news propagation graphs that generalise to OOD data, using a graph neural network-based mask generation process. It uses refined training objectives to ensure high-quality subgraphs. It is further powered by contrastive learning for few-shot scenarios, where a limited amount of OOD data is available for training. Extensive experiments on public social media datasets demonstrate that CSDA effectively handles OOD misinformation detection, achieving a 1.23%∼12.23% accuracy improvement over other state-of-the-art models, covering OOD news domains in politics, entertainments, health, etc.
Uncovering the Internet’s Hidden Values: An Empirical Study of Desirable Behavior Using Highly-Upvoted Content on Reddit
A major task for moderators of online spaces is norm-setting, essentially creating shared norms for user behavior in their communities. Platform design principles emphasize the importance of highlighting norm-adhering examples and explicitly stating community norms. However, norms and values vary between communities and go beyond content-level attributes, making it challenging for platforms and researchers to provide automated ways to identify desirable behavior to be highlighted. Current automated approaches to detect desirability are limited to measures of prosocial behavior, but we do not know whether these measures fully capture the spectrum of what communities value. In this paper, we use upvotes, which express community approval, as a proxy for desirability and examine 16,000 highly-upvoted comments across 80 popular sub-communities on Reddit. Using a large language model, we extract values from these comments across two years (2016 and 2022) and compile 64 and 72 macro, meso, and micro values for 2016 and 2022 respectively, based on their frequency across communities. Furthermore, we find that existing computational models for measuring prosociality were inadequate to capture on average 82% of the values we extracted. Finally, we show that our approach can not only extract most of the qualitatively-identified values from prior taxonomies, but also uncover new values that are actually encouraged in practice. Our findings highlight the need for nuanced models of desirability that go beyond preexisting prosocial measures. This work has implications for improving moderator understanding of their community values and provides a framework that can supplement qualitative approaches with larger-scale content analyses.
What’s Political on TikTok? Perceptions, Prevalence, and Patterns of Exposure to TikToks Users Perceive as Political
As its popularity soars across the U.S., TikTok has begun to draw criticism pertaining to political content on its platform. We conducted a study of 366 participants who used our browser-based tool to annotate videos from their personal TikTok feeds, marking those they perceived as political and briefly explaining why. We analyze the resulting data both qualitatively and quantitatively to answer three research questions investigating perceptions, prevalence, and patterns of user exposure to political content on TikTok. We answer these questions through qualitative coding of participants' responses, measuring rates of exposure to content users perceive as political, and running statistical tests to analyze patterns of overall and topic-specific exposure in relation to participant attributes. We developed a codebook with 32 political topics present in participant descriptions of political content, find that perceived-political content comprises 16.85% of participants' feeds on average, and that participants' age and interest in politics are associated with higher overall exposure to perceived-political content while gender and political ideology are associated with exposure to topic-specific content about gender and sexuality rights. We conclude with implications for user-centered studies of TikTok and other social media platforms, and directions for future work.
Using Salient Object Detection to Identify Manipulative Cookie Banners That Circumvent GDPR
The main goal of this paper is to study how often cookie banners that comply with the General Data Protection Regulation (GDPR) contain aesthetic manipulation, a design tactic to draw users’ attention to the button that permits personal data sharing. As a byproduct of this goal, we also evaluate how frequently the banners comply with GDPR and the recommendations of national data protection authorities regarding banner designs. We visited 2,579 websites and identified the type of cookie banner implemented. Although 45% of the relevant websites have fully compliant banners, we found aesthetic manipulation on 38% of the compliant banners. Unlike prior studies of aesthetic manipulation, we use a computer vision model for salient object detection to measure how salient (i.e., attention-drawing) each banner element is. This enables the discovery of new types of aesthetic manipulation (e.g., button placement), and leads us to conclude that aesthetic manipulation is more common than previously reported (38.0% vs 27.0% of banners). To study the effects of user and/or website location on cookie banner design, we include websites within the European Union (EU), where privacy regulation enforcement is more stringent, and websites outside the EU. We visited websites from IP addresses in the EU and from IP addresses in the United States (US). We find that 13.9% of EU websites change their banner design when the user is from the US, and EU websites are roughly 48.3% more likely to use aesthetic manipulation than non-EU websites, highlighting their innovative responses to privacy regulation.
Language-Grounded Co-evolution of Opinions and Ties: Paired Rewiring with Large Language Model Agents
Modeling how user opinions and social ties co-evolve is central to understanding polarization and community formation in online platforms, yet existing approaches often sacrifice either linguistic realism or network structure. We propose a simulation framework for studying the co-evolution of online networks and opinions, in which large language models (LLMs) handle opinion updating, content generation, and network rewiring. This design integrates linguistic signals into structural change under real-world constraints, enabling systematic comparison across LLMs and empirical validation on three real-world datasets. Results show that LLM-based simulations consistently outperform equation-based baselines across key network metrics and better reproduce observed stance shifts. Analysis of the reasoning behind model decisions reveals systematic differences in rewiring motives and highlights distinct tendencies across models, offering interpretive insight into the network structures they produce. In addition, varying rewiring probabilities and strategies demonstrate how platform-like controls shape polarization and cohesion. Together, these findings establish a language-grounded, empirically validated approach to modeling network–opinion co-evolution with greater accuracy and interpretability.
SoMeR: A Multi-View Social Media User Representation Learning Framework
Low-dimensional representations of social media users are crucial for modeling preferences, interests, and behavior, with applications ranging from behavior prediction to the detection of inauthentic accounts. However, existing approaches often focus narrowly on specific tasks or data modalities such as text, activity patterns, or platform metadata, limiting their ability to holistically capture the complexity of user behavior. We introduce SoMeR: Social Media user Representation learning, a multi-view framework that integrates temporal activity, textual content, profile attributes, and network interactions to learn comprehensive user embeddings. SoMeR encodes users’ post streams as sequences of time-stamped text features, embeds them using transformers alongside profile data, and jointly trains on link prediction and contrastive objectives to learn representations that reflect both behavioral patterns and social similarity. We demonstrate the versatility and effectiveness of SoMeR in three applications: (1) identifying accounts driving information operations, (2) measuring online polarization following major events, and (3) predicting future participation in Reddit hate communities. By modeling user behavior across multiple modalities and tasks, SoMeR enables a deeper understanding of socio-political dynamics on social media and supports more informed interventions.
Assessing How Hate, Counterspeech, and Toxicity Affect Hate Group Newcomers
Counterspeech has gained attention as a strategy to reduce hate speech on social media. Although previous studies suggest that counterspeech can reduce hate speech, little is known about its effects on participation in online hate communities. Relatedly, we lack an understanding about the degree of hostility in counterspeech. Hostile counterspeech may increase online conflict, potentially hardening the positions of hate adherents, and further eroding online environments. Here, we analyzed the effect of counterspeech on 16,513 newcomers across 104 hate subreddits (forums within Reddit.com). We devised an LLM-based counterspeech detection approach that outperforms specialized models trained on existing datasets, then examined the presence, and effects of, hostility. While counterspeech comments are less toxic than hate speech comments, they are almost twice as toxic as other discourse within hate subreddits. We then evaluated the effect of counterspeech on newcomer engagement in hate subreddits. We found that newcomers using hate speech who receive counterspeech are less likely to continue posting within these hate subreddits, rather than becoming galvanized. We speculate that, instead of constituting ardent hate adherents, readily-dissuaded newcomers may merely be toying with beliefs that are proscribed in other contexts. Although we found no association between the toxicity of counterspeech and its effects on user retention, consistent with prior research regarding the harmful effects of toxic speech, we found that toxic counterspeech increases the probability of continued hostility from hate users within the same discussion.
Can Social Networks Substitute for Reputation Systems? Evidence from a Large Scale Field Experiment
Reputation systems are considered by many to be foundational to online peer-to-peer marketplaces. Both economic theory and previous empirical work suggest that centralized reputation should increase trust, increase trustworthiness, and foster marketplace growth. However, prior work also suggests that embedding peer-to-peer commerce in social settings can create trust and obviate the need for centralized reputation. Thus, it is unclear whether we should expect centralized reputation to matter in a setting where social network features are also generating trust. In this paper, we present results from what we believe is the first ever field experiment to randomize the introduction of a reputation system into a massive online marketplace. The setting for our study is an online peer-to-peer marketplace embedded inside of a large social network. We find that on average, the introduction of ratings did not have an effect on the amount of marketplace activity, the behavior of marketplace users, or the structure of the marketplace in the six months following its introduction. Our results suggest that centralized reputation may be unnecessary when online marketplaces are embedded in social networks. Given the number of previously documented biases that can arise in centralized reputation systems, the use of social network data may provide an attractive alternative for online marketplace designers.
Auditing Google’s AI Overviews and Featured Snippets: A Case Study on Baby Care and Pregnancy
Google Search increasingly surfaces AI-generated content through features like AI Overviews (AIO) and Featured Snippets (FS), which users frequently rely on despite having no control over their presentation. Through a systematic algorithm audit of 1,508 real baby care and pregnancy-related queries, we evaluated the quality and consistency of these information displays. Our robust evaluation framework assessed multiple quality dimensions including answer consistency, relevance, presence of medical safeguards, source categories, and sentiment alignment. Results reveal concerning gaps in information consistency, with information in AIO and FS displayed on the same search result page being inconsistent with each other in 33% of cases. Despite high relevance scores, both features critically lacked medical safeguards (present in just 11% of AIO and 7% of FS responses). While health and wellness websites dominated source categories for both, AIO and FS, FS also often linked to commercial sources. These findings have important implications for public health information access and demonstrate the need for stronger quality controls in AI-mediated health information. Our methodology provides a transferable framework for auditing AI systems across high-stakes domains where information quality directly impacts user wellbeing.
Detecting Coordinated Activities Through Temporal, Multiplex, and Collaborative Analysis
In the era of widespread online content consumption, effective detection of coordinated efforts is crucial for mitigating potential threats arising from information manipulation. Despite advances in isolating inauthentic and automated actors, the actions of individual accounts involved in influence campaigns may not stand out as anomalous if analyzed independently of the coordinated group. Given the collaborative nature of information operations, coordinated campaigns are better characterized by evidence of similar temporal behavioral patterns that extend beyond coincidental synchronicity across a group of accounts. We propose a framework to model complex coordination patterns across multiple online modalities. This framework utilizes multiplex networks to first decompose online activities into different interaction layers, and subsequently aggregate evidence of online coordination across the layers. In addition, we propose a time-aware collaboration model to capture patterns of online coordination for each modality. The proposed time-aware model builds upon the node-normalized collaboration model and accounts for repetitions of coordinated actions over different time intervals by employing an exponential decay temporal kernel. We validate our approach on multiple datasets featuring different coordinated activities. Our results demonstrate that a multiplex time-aware model excels in the identification of coordinating groups, outperforming previously proposed methods in coordinated activity detection.
Understanding the Complexities of Responsibly Sharing NSFW Content Online
Reddit is in the minority of mainstream social platforms that permit posting content that may be considered to be at the edge of what is permissible, including so-called Not Safe For Work (NSFW) content. However, NSFW is becoming more common on mainstream platforms, with X now allowing such material. We examine the top 15 NSFW-restricted subreddits by size to explore the complexities of responsibly sharing adult content, aiming to balance ethical and legal considerations with monetization opportunities. We find that users often use NSFW subreddits as a social springboard, redirecting readers to private or specialized adult social platforms such as Telegram, Kik or OnlyFans for further interactions. They also directly negotiate image "trades" through credit cards or payment platforms such as PayPal, Bitcoin or Venmo. Disturbingly, we also find linguistic cues linked to non-consensual content sharing. To help platforms moderate such behavior, we trained a RoBERTa-based classification model that outperforms GPT-4 and traditional classifiers, such as logistic regression and random forest, in identifying non-consensual content sharing, showing better performance in this specific task.
Filling the Blanks: Context-driven Detection and Correction of Cherry-picking in News Reporting
Cherry-picking involves suppressing (censoring) or distorting evidence that supports the counter argument. Cherry-picking facts in news reports by mainstream media distorts public perception, undermines trust, and fuels misinformation by presenting a biased or incomplete narrative. Manually identifying suppressed statements in news stories is challenging and time consuming. In this study, we introduce a novel importance-based approach to automatically spotting and correcting cherry-picking by identifying then substituting missing important statements in a target news story with the help of contextual information from other news sources with different biases. Additionally, we showcase the flexibility of our approach by utilizing different methods to estimate a statement's importance including fine-tuned embedding models, zero and few-shot generative models, in addition to unsupervised methods. Furthermore, this research introduces a novel dataset specifically designed for training and evaluating cherry-picking detection methods. Our best performing method achieves an F-1 score of above 90% in estimating a statement's importance. Moreover, results show the effectiveness of the proposed approach in correcting cherry-picking and bringing the biased narrative closer to its neutral alternative by nearly 12%. Finally, through thorough experimentation, we provide answers to a set of important research questions related to cherry-picking detection and correction.
Can Large Language Models Assess the Social Impact of Conspiracy Theories?
While Large Language Models (LLMs) can identify conspiracy theories (CTs), their real-world harmful impacts vary significantly and remain unclear. We therefore ask: Can LLMs serve as automated agents for social impact assessment of CTs? Our preliminary study with vanilla prompts reveals that LLMs fail to provide accurate impact assessments because of two key limitations. First, LLMs are good at retrieving CT-related information but struggle with fine-grained analysis and comparisons. Second, their assessments are highly sensitive to the way CTs are presented and framed in the prompt, inducing systematic biases. Drawing inspiration from social science practices, we design tailored strategies to enable LLMs to mimic human-like impact assessment. We benchmark several state-of-the-art LLMs against survey and social media data capturing human-perceived CT impacts. Our experiments demonstrate that an impact assessment framework employing multi-step analysis and comparisons to investigate diverse CT-related information can deliver more reliable results. Finally, we discuss promising solutions to mitigate the influence of prompting biases.
How Growing Toxicity Manifests: A Topic Trajectory Analysis of U.S. Immigration Discourse on Social Media
In the online public sphere, discussions about immigration often become increasingly fractious, marked by toxic language and polarization. Drawing on 4 million X posts over six months, we combine a user- and topic-centric approach to study how shifts in toxicity manifest as topical shifts. Our topic discovery method, which leverages instruction-based embeddings and recursive HDBSCAN, uncovers 157 fine-grained subtopics within the U.S. immigration discourse. We focus on users in four groups: those with increasing toxicity, those with decreasing toxicity, and two reference groups with no significant toxicity trend but matched toxicity levels. Treating each posting history as a trajectory through a five-dimensional topic space, we compare average group trajectories using permutational MANOVA. Our findings show that users with increasing toxicity drift toward alarmist, fear-based frames, whereas those with decreasing toxicity pivot toward legal and policy-focused themes. Both patterns diverge statistically significantly from their reference groups. This pipeline, which combines hierarchical topic discovery with trajectory analysis, offers a replicable method for studying dynamic conversations around social issues at scale.
Reshaping of Mental Health Discourse on Reddit: The Rise of Neurodivergence
Public discourse about mental health is changing rapidly, and these changes are increasingly playing out online. Previous research has explored how specific mental health conditions are represented on social media, but little is known about representations of the wider mental health domain or how these representations have evolved over time. Analyzing posts and comments from 14 mental health subreddits (2015–2022), we characterize shifts in affinities among subreddits based on language and user activity. We also examine whether changes in mental health discourse reflect changes in attention (salience) to different conditions. Reddit mental health discourse was substantially reshaped over the study period, with autism and ADHD becoming more central within the network. Moreover, rises in a condition's centrality within the network were associated with increases in its salience. Our findings indicate that public understandings may be changing not only for individual conditions but also in how mental health and disorder are understood more broadly. These shifts may be contributing to discrepancies between public and professional understandings of mental health problems and have implications for help-seeking behaviors, overdiagnosis, and misdiagnosis. As public attention to mental health grows, understanding these dynamics is becoming increasingly important.
When ‘For You’ Isn’t for You: Measuring User Agency in TikTok’s Algorithmic Feed
The short-form video-sharing service TikTok has become an important platform in the social media landscape, with much of its popularity owed to its algorithmically-driven "For You Page'' (FYP). This feature serves as the "home screen'' for the platform and provides a personalized feed of content for each user. Unlike other social media services---where new users start their journey by explicitly signaling whom they choose to friend or follow---the TikTok FYP algorithm instead begins making inferences based on implicit signals, such as how long they watch particular videos. As a result, users have less explicit control over what content they see, and concerns have been raised about the impact on users (e.g., the delivery of potentially harmful content). In this work, we investigate the extent to which users have control over the content they see on the FYP on TikTok. We first develop novel techniques to study the TikTok mobile app, introducing a new avenue for conducting controlled experiments that enable us to send both explicit and implicit signals on the app. We then use these techniques to study the FYP algorithm based on accounts we control. We find that the FYP algorithm is sensitive to both types of signals, changing the amount of personalized content the account sees. However, we find that users may have difficulty convincing the FYP algorithm to stop showing content the user wishes to no longer see: the most effective explicit signal---marking a video as `Not Interested'---is unintuitively buried in the interface. Worse, we find that once accounts cease to indicate disinterest in a topic, many find their feeds dominated by such content again.
Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users
Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short span, yet, amidst the stream of genuine information, fake news and rumors continue to spread. The need for effective detection models is becoming increasingly critical. Past user behavior and user engagement on a post are strong signals that SOTA approaches leverage for fake news detection and other post classification tasks. However, these approaches lean too heavily on knowing this past behavior, and thus suffer from a cold user problem, or users that are new or have minimal footprint on the platform. In this paper, we make three core contributions. We first establish the value of user behavior, both content and user-user interactions, in the task of fake news and rumor detection. We then establish the extensive prevalence of cold users in the real-world datasets, and show the need for newer algorithms considering cold users. We next propose a novel socially-aware context representation scheme – USER EVIDENCE NETWORK (UEN) – to detect the spread of misinformation and unverified information while efficiently navigating this cold user challenge. We introduce techniques that approximate missing/absent behavior data of a new user from existing users' interactions. By carefully addressing the cold user challenge, our work provides robust approaches targeting fake news and rumor detection for real-world platforms.
Measuring the Semantic Structure and Evolution of Conspiracy Theories
Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings change over time. This gap persists partly because conspiracy-related terms are often treated as stable lexical markers, making it difficult to separate genuine semantic changes from surface-level vocabulary changes. In this paper, we measured the semantic structure and evolution of conspiracy theories in online political discourse. Using 169.9M comments from Reddit’s r/politics subreddit spanning 2012-2022, we first demonstrated that conspiracy- related language forms coherent and semantically distinguishable regions of language space, allowing conspiracy theories to be treated as semantic objects. We then tracked how these objects evolved over time using aligned word embeddings, enabling comparisons of semantic neighborhoods across periods. Our analysis revealed that conspiracy theories evolve non-uniformly, exhibiting patterns of semantic stability, expansion, contraction, and replacement that are not captured by keyword-based approaches alone.
Digital Diasporas: How Origin Characteristics and Host–Native Distance Shape Immigrants’ Online Cultural Retention
Immigrants bring unique cultural backgrounds to their host countries. Subsequent interplay of cultures can lead to either a melting pot, where immigrants adopt the dominant culture of the host country, or a mosaic, where distinct cultural identities coexist. The existing literature primarily focuses on the acculturation of immigrants, specifically the melting pot hypothesis. In contrast, we attempt to identify the antecedents of the mosaic hypothesis or factors that enhance (or diminish) the propensity for cultural retention among immigrants. Based on Facebook advertising data for immigrants from 8 countries residing in the USA, our findings suggest that greater host–native distance is linked to higher online cultural retention, and while origin country context is statistically significant, its impact is generally smaller.
Moral Framing and Online Fundraising Outcomes: Evidence from GoFundMe Campaigns
This study examines the relationship between moral framing and fundraising outcomes, encompassing both monetary and social support, through an analysis of 14,088 GoFundMe campaigns. Focusing on three moral foundations (care, fairness, and ingroup loyalty), we quantified the presence of these frames within campaign appeals. Our results show that the association between moral framing and campaign success varies significantly across fundraising categories. Specifically, negatively framed appeals emphasizing the harm experienced by the help-seeker were associated with a higher volume of donations and comments in the Emergency and Memorial categories, yet related to fewer donations in animal-related campaigns. Highlighting harm was not associated with a higher average donation amount per donor. Ingroup loyalty framing was positively associated with both donation volume and supporter engagement. This research extends the literature on moral communication in crowdfunding and offers practical implications for the design of online fundraising platforms to better facilitate prosocial interactions between fundraisers and supporters.
The Spread of Virtual Gifting in Live Streaming: The Case of Twitch
This paper examines how gifting spreads among viewers on Twitch, one of the largest live streaming platforms worldwide. On Twitch, users can purchase gift subscriptions for other viewers and often opt for community gifting, in which subscriptions are distributed to randomly selected viewers. Leveraging this random distribution as a natural experiment, we investigate the conditions under which gift recipients pay it forward to others. Our findings show that receiving a gift significantly increases the likelihood to pay it forward. This positive effect is amplified in single-recipient gifting events (i.e., one subscription is given to one individual per event) compared to mass distributions involving multiple recipients. Conversely, receiving a gift from a frequent gifter can attenuate the willingness to pay it forward. Finally, anonymous gifting has no significant influence on the spread of gifting. This research contributes to the literature on the spread of online prosocial behavior by providing robust empirical evidence and offers actionable insights for platform designers seeking to promote and sustain prosocial interactions in online communities.
Disproportionate Voices: Participation Inequality and Hostile Engagement in News Comments
Digital platforms were expected to foster broad participation in public discourse, yet online engagement remains highly unequal and underexplored. We ask whether who talks the most also shapes the tone that becomes visible in news comment sections. Using 260 million comments from 6.2 million users over 13 years on Naver News, we measure participation inequality with the Gini and Palma indexes and estimate hostility levels with a KC-Electra model, which outperformed other Korean pre-trained transformers in multi-label classification tasks. We test two hypotheses: (H1) a within-section, over-time association between participation concentration and hostile tone; and (H2) a decomposition of hostility changes into composition (who talks how much) versus behavior (how those same groups talk). We find that higher concentration is associated with higher hostile tone, and that changes in hostility are driven mainly by behavioral intensification among active users, rather than compositional shifts. These results quantify the relationship suggested by exploratory patterns and point to an amplification risk: when a few users dominate, visible discourse can skew sharper, potentially discouraging casual participation and reinforcing concentration.
Adverse Mental Health Events as a Trigger for Online Harassment: Evidence from Online Conversations of Youth Experiencing Psychiatric Hospitalizations
Online harassment is a pervasive issue that disproportionately affects individuals with mental health challenges. Yet, modeling under what circumstances an individual might become susceptible to harassment remains under-explored. This study investigates how psychiatric hospitalization may causally impact the likelihood of experiencing online harassment, using over 360,000 Instagram direct messages shared by individuals with and without mental health diagnoses. Our analysis reveals distinct linguistic patterns in harassment directed at mental health patients, with a significant increase in harassment following hospitalization. A Difference-in-Differences analysis highlights the critical two-week period post-hospitalization as a time of heightened risk to harassment. Additionally, we explore the protective role of social support and resilience through Vector Autoregressive modeling, showing that emotional and informational support significantly reduce the likelihood of harassment. These findings emphasize the need for context-aware interventions and trauma-informed platform designs to protect vulnerable individuals online. By understanding the interplay between mental health events, social support, and online harassment, this research offers new directions for developing strategies to foster safer digital environments. Content Warning: This paper includes mentions and descriptions of online harassment and mental health challenges.
Belief Alignment vs Opinion Leadership: Understanding Cross-linguistic Digital Activism in K-pop and BLM Communities
The internet has transformed activism, giving rise to more organic, diverse, and dynamic social movements that transcend geo-political boundaries. Despite extensive research on the role of social media and the internet in cross-cultural activism, the fundamental motivations driving these global movements remain poorly understood. This study examines two plausible explanations for cross-cultural activism: first, that it is driven by influential online opinion leaders, and second, that it results from individuals resonating with emergent sets of beliefs, values, and norms. We conduct a case study of the interaction between K-pop fans and the Black Lives Matter (BLM) movement on Twitter following the murder of George Floyd. Our findings provide strong evidence that belief alignment, where people resonate with common beliefs, is a primary driver of cross-cultural interactions in digital activism. We also demonstrate that while the actions of potential opinion leaders—in this case, K-pop entertainers—may amplify activism and lead to further expressions of love and admiration from fans, they do not appear to be a direct cause of activism. Finally, we report some initial evidence that the interaction between BLM and K-pop led to slight increases in their overall belief similarity.
Alexandria: A Library of Pluralistic Values for Realtime Re-Ranking of Social Media Feeds
Social media feed ranking algorithms fail when they too narrowly focus on engagement as their objective. The literature has asserted a wide variety of values that these algorithms should account for as well---ranging from well-being to productive discourse---far more than can be encapsulated by a single topic or theory. In response, we present a library of values for social media algorithms: a pluralistic set of 78 values as articulated across the literature, implemented into LLM-powered content classifiers that can be installed individually or in combination for real-time re-ranking of social media feeds. We investigate this approach by developing a browser extension, Alexandria, that re-ranks the X/Twitter feed in real time based on the user's desired values. Through two user studies, both qualitative (N=12) and quantitative (N=257), we found that diverse user needs require a large library of values, enabling more nuanced preferences and greater user control. With this work, we argue that the values criticized as missing from social media ranking algorithms can be operationalized and deployed today through end-user tools.
Beyond Metadata: Multimodal, Policy-Aware Detection of YouTube Scam Videos
YouTube is a major platform for information and entertainment, but its wide accessibility also makes it attractive for scammers to upload deceptive or malicious content. Prior detection approaches rely largely on textual or statistical metadata, such as titles, descriptions, view counts, or likes, which are effective in many cases but can be evaded through benign-looking text, manipulated statistics, or other obfuscation strategies (e.g., ‘Leetspeak’), while ignoring visual cues. In this study, we systematically investigate multimodal approaches for detecting YouTube scams. Our dataset consolidates established scam categories and augments them with full-length videos and policy-grounded reasoning annotations. Experiments show that a text-only model using titles and descriptions (fine-tuned BERT) achieves moderate performance (76.61% F1 score), improving slightly with audio transcripts (77.98% F1 score). Visual analysis with a fine-tuned LLaVA-Video model performs better (79.61% F1 score), while a multimodal framework combining titles, descriptions, and video frames achieves the highest performance (82.96% F1 score). Moreover, the multimodal framework showed greater robustness to adversarial perturbations, with accuracy dropping only 1–3%, compared to 12–38% for modality-specific models. Beyond accuracy, the multimodal framework provides interpretable, policy-grounded reasoning, enhancing transparency and practical utility in automated moderation. Using this approach, we analyzed 6,374 in-the-wild YouTube videos and detected 1,864 scams with explicit reasoning, providing a valuable resource for future research.
Cross-Language Evolution of Divergent Collective Memory Around the Arab Spring
The Arab Spring was a cascade of protests and uprisings across the Middle East and North Africa that toppled governments and escalated into prolonged conflicts in others. Collective memories of these events evolve unevenly across political, cultural, and linguistic contexts. Drawing on archived content from the Arabic and English Wikipedias (2011-2024), this study provides a generalizable framework for studying long-term collective memory processes on Wikipedia. We propose multilingual measures of salience, deliberation, contextualization, and consolidation and evaluate them using English and Arabic Wikipedia articles about the Arab Spring. Article size and wikilinks indicate sustained salience in both languages, though along different trajectories. Temporal clustering of wikilink inclusion reveals Stable, Debated, and Forgotten content, marking zones of consensus, contestation, and exclusion. Interlanguage links show limited cross-lingual alignment, with only a minority shared between editions. English pages consistently link back to the Arab Spring summary, while Arabic pages often do not, yielding uneven integration of the phenomenon into the encyclopedic network. These findings demonstrate Wikipedia's role in long-term memory work and highlight cross-lingual biases relevant to multilingual knowledge systems.
Unsupervised Detection of Persistent Communities in Dynamic Networks with Network Embeddings
Many real-world phenomena can be modeled using dynamic networks organized according to an evolving community structure. While traditional community discovery algorithms rely on structural information, recent years have seen a shift in focus towards methods of embedding nodes in dynamic graphs that incorporate temporal information through times- tamps and contextual information through features. This en- deavour is particularly evident in Graph Neural Networks (GNNs). In this paper, we address the importance of node embeddings and features in understanding network dynamics. First, we propose a general framework for extracting dynamic communities from graph embeddings without prior knowl- edge of these communities. We then design a synthetic dy- namic graph generator that produces network data and fea- tures for GNN training. Experiments on synthetic dynamic networks demonstrate that informative features are essential for ensuring the performance of GNNs. By comparing several embedding algorithms on different scenarios, we demonstrate that our method effectively captures the network’s underlying dynamics at both the macro and node levels. Furthermore, we demonstrate that GNN-based embeddings are more effective at capturing global network dynamics. They significantly out- perform conventional embedding or community detection al- gorithms when it comes to detecting changes in dynamics at the node level. Additionally, we tested our method on a real- world dataset of social media interactions, achieving promis- ing results for network visualization.
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems
The proliferation of generative models has presented significant challenges in distinguishing authentic human-authored content from deepfake content. Collaborative human efforts, augmented by AI tools, present a promising solution. In this study, we explore the potential of DeepFakeDeLiBot, a deliberation-enhancing chatbot, to support groups in detecting deepfake text. Our findings reveal that group-based problem-solving significantly improves the accuracy of identifying machine-generated paragraphs compared to individual efforts. While engagement with DeepFakeDeLiBot does not yield substantial performance gains overall, it enhances group dynamics by fostering greater participant engagement, consensus building, and the frequency and diversity of reasoning-based utterances. Additionally, participants with higher perceived effectiveness of group collaboration exhibited performance benefits from DeepFakeDeLiBot. These findings underscore the potential of deliberative chatbots in fostering interactive and productive group dynamics while ensuring accuracy in collaborative deepfake text detection.
Asymmetric Dynamics of Partisan Warriors in YouTube Comments
Cross-cutting commenting on social media is often imagined as a path to deliberation, yet exposure to opposing views frequently fuels hostility. To explain this dynamic, we introduce the concept of partisan warriors—commenters who cross ideological lines primarily to launch uncivil attacks against out-partisans. We analyze a large corpus of YouTube comments (N= 1,854,320) surrounding the 2024 U.S. second presidential debate. After filtering for toxicity and active participation, we use large language models to identify attack targets and operationalize partisan warrior behavior. Our analysis highlights four dynamics. First, cross-cutting commenters do not exhibit greater civility than those who remain within their ideological camps (RQ1). Second, audience reactions diverge by ideology: conservative audiences tended to reward hostile attacks on out-group leaders, whereas liberal audiences offered no comparable incentives and at times penalized such attacks (RQ2). Third, partisan warriors are notably more prevalent in conservative-leaning channels than in liberal ones; commenters restricted to conservative spaces were substantially more likely to engage in partisan warrior behavior compared to their liberal-only counterparts (RQ3). Finally, regarding environmental triggers, robustness checks suggest that this participation is an ecological phenomenon driven largely by channel-level heterogeneity rather than transient responses to individual video titles (RQ4). By shifting attention from the prevalence of incivility to its targets, rewards, and structural drivers, this study advances understanding of how partisan hostility is enacted and sustained in online spaces.
A Large-Scale Analysis of Public-Facing, Community-Built Chatbots on character.AI
This paper presents the first large-scale analysis of public-facing chatbots on Character.AI, a rapidly growing social media platform where users create and interact with chatbots. Character.AI is distinctive in that it merges generative AI with user-generated content, enabling users to build bots for others to engage with. It is also popular, with over 20 million monthly active users, and impactful, with headlines detailing significant issues with youth engagement on the site. Character.AI is thus of interest to study both substantively and conceptually. To this end, we present a descriptive overview using a dataset of 2.1 million English-language prompts (or “greetings”) from chatbots on the site, created by around 1 million users. Our work explores the prevalence of different fandoms on the site, broader tropes that persist across fandoms, and how dynamics of power intersect with gender within greetings. Overall, our findings illuminate an emerging form of online (para)social interaction at a unique and important intersection between generative AI and user-generated content.
User-Side Interventions Reduce Harmful Content Exposure in Algorithmic Feeds
Recommendation algorithms on social media platforms optimize for user engagement, which can inadvertently amplify exposure to harmful content such as violence, sexual material, and hate speech. Platform-level moderation is often delayed, opaque, and uniform, motivating the need for complementary user-side interventions that allow individuals to reduce unwanted content in their feeds without relying on platform cooperation. Prior work largely relies on single-session or human-subject studies, limiting the ability to capture recursive recommendation feedback loops, control for users’ baseline preferences for harmful content, or systematically compare intervention strategies across harm types. To address these gaps, we propose a sock puppet simulation framework that models 30 rounds of iterative recommendation and interaction. We evaluate two user-side interventions: Downranking and Replacement, on YouTube’s Homepage and Up-Next interfaces, controlling for users’ baseline harm exposure levels (0%, 25%, 50%), yielding 18 experimental conditions with 1,000 puppets each. Our results show that user-side interventions are effective relative to the baseline, with effects concentrated on the Homepage interface. In particular, Downranking emerges as the most robust and consistent strategy, producing statistically significant improvements in both final-state outcomes (net change) and cumulative harmful exposure across baseline preference levels. For example, Downranking reverses baseline increases in harm into significant decreases (e.g., from a +0.6 percentage-point increase to a -0.9 percentage-point reduction), and yields durable reductions in cumulative exposure over time. Replacement shows weaker and less consistent effects on final-state outcomes. We further find no strong evidence of heterogeneous intervention effects across harm types, and observe that the overall reduction in harmful recommendations is largely driven by declines in Physical harm. Our work establishes the long-term efficacy of user-side interventions and provides guidance for their design.
Who Talks to Whom: Quantifying Echo Chamber Effects in Emerging Social Media Platforms
This study investigates interaction patterns on three emerging social media platforms in the "post-Twitter era"—BlueSky, Mastodon, and Truth Social—focusing on reply networks as highly interactive, opinion-expressive forms of engagement within a polarization context. We employ a multi-method approach to quantify the echo chamber effect, analyzing (1) the role of influential users in shaping narratives (using PageRank and LexRank), (2) ideological distribution within reply network communities (leveraging the Leiden algorithm for community detection and LLMs to assess ideological leanings), and (3) patterns of connectivity favoring ideologically similar users (via biased random walks). To ensure rigorous cross-platform comparability, we apply strict controls on topic and timeframe. Our findings reveal a relatively weaker echo chamber effect on Mastodon and BlueSky, though the platforms display subtle differences: Mastodon fosters a diverse ideological landscape with frequent cross-ideological exchanges, while discussions on BlueSky are more centered around moderate voices. In contrast, Truth Social exhibits a pronounced echo chamber effect, with the majority of users supporting right-leaning or pro-Trump narratives, in alignment with the platform’s founding intent as a dedicated space for Trump supporters. Despite this homogeneity, we observe occasional instances of critical engagement and fact-checking on Truth Social, suggesting that even within ideologically driven spaces, counter-narratives can emerge, though their influence remains uncertain. These findings underscore the need for further research into the unique dynamics of emerging social media platforms, particularly those shaped by ideological leanings. Understanding the roles echo chambers play in shaping public opinion is essential for gauging their impact on (mis)information diffusion and political polarization in an increasingly fragmented digital landscape.
“Harmless to You, Hurtful to Me!”: Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth
Risk perception is subjective, and youth's understanding of toxic content differs from that of adults. Although previous research has conducted extensive studies on toxicity detection in social media, the investigation of youth's unique toxicity, i.e., languages perceived as nontoxic by adults but toxic as youth, is ignored. To address this gap, we aim to explore: 1) What are the features of ``youth-toxicity'' languages in social media (RQ1); 2) Can existing toxicity detection techniques accurately detect these languages (RQ2). For these questions, we took Chinese youth as the research target, constructed the first Chinese ``youth-toxicity'' dataset, and then conducted extensive analysis. Our results suggest that youth's perception of these is associated with several contextual factors, like the source of an utterance and text-related features. Incorporating these meta information into current toxicity detection methods significantly improves accuracy overall. Finally, we propose several insights into future research on youth-centered toxicity detection.
SCRum-9: Multilingual Stance Classification over Rumours on Social Media
We introduce SCRum-9, the largest multilingual Stance Classification dataset for Rumour analysis in 9 languages, containing 7,516 tweets from X. SCRum-9 goes beyond existing stance classification datasets by covering more languages, linking examples to more fact-checked claims (2.1k), and including confidence-related annotations from multiple annotators to account for intra- and inter-annotator variability. Annotations were made by at least two native speakers per language, totalling more than 405 hours of annotation and 8,150 dollars in compensation. Further, SCRum-9 is used to benchmark five large language models (LLMs) and two multilingual masked language models (MLMs) in In-Context Learning (ICL) and fine-tuning setups. This paper also innovates by exploring the use of multilingual synthetic data for rumour stance classification, showing that even LLMs with weak ICL performance can produce valuable synthetic data for fine-tuning small MLMs, enable them to achieve higher performance than zero-shot ICL. Finally, we examine the relationship between model predictions and human uncertainty on ambiguous cases finding that model predictions often match the second-choice labels assigned by annotators, rather than diverging entirely from human judgments. SCRum-9 is publicly released to the research community with potential to foster further research on multilingual analysis of misleading narratives on social media.
From Pixels to Personas: Tracking the Evolution of Anime Characters
Anime, originated from Japan, is one of the most influential cultural products in modern society and is especially popular among younger generations. The popularity of anime reflects important cultural evolutions in our society. Despite existing research on anime as a cultural phenomenon, we still have a limited understanding of how anime really evolves over the years. In this study, using a large-scale multimodal dataset of anime characters from an anime review site, we applied computational methods that integrate textual, visual, and production features of anime characters with online popularity traces. By combining LLM-extracted personality features with avatar features, we identify recurring personality archetypes and visual tropes with their temporal evolution over the past decades. We found that the target audience of anime has undergone a systematic shift from children to a maturing audience of teenagers and young adults over time. Character design has been undergoing moe-ification, with softer or sexualized female traits becoming increasingly prominent since the 2000s. Some personality archetypes are often visually predictable, yet audiences also tend to prefer less conventionalized characters. Finally, we reveal that visual signals play a more dominant role than personality traits in shaping audience preferences, with features such as moe-style faces and mechanical designs contributing greatly to popularity. These findings offer insights into the broader dynamics of anime's cultural and creative practices.
Structural Dynamics of Harmful Content Dissemination on WhatsApp
WhatsApp, a platform with over two billion global users, plays a pivotal role in digital communication but also serves as a vector for harmful content such as misinformation, hate speech, and political propaganda. This study examines the dynamics of harmful message dissemination in WhatsApp groups, with a focus on their structural characteristics. Utilizing a comprehensive dataset of over 5.1 million messages—including text, images, and videos—collected from approximately 6,000 groups in India, we reconstruct message propagation cascades to analyze patterns of dissemination. Our findings reveal that harmful messages consistently achieve greater depth and breadth of dissemination compared to normal messages, with videos and images emerging as the primary modes of spread. These results suggest a distinctive dissemination pattern for harmful content. However, our analysis indicates that modality alone cannot fully account for the structural differences in propagation. Finally, using the reconstructed cascades and the spreading patterns, we estimate the potential reach of various types of harmful content. We find that harmful viral content reaches almost 5 times more users compared to normal content. The findings highlight the critical role of structural characteristics in the spread of these harmful messages, suggesting that strategies targeting structural characteristics of message chains could be crucial in managing the dissemination of such content on private messaging platforms.
Claim Verification with Adversarial Reasoning and Planning
The scale and speed of digital communication demands robust automated claim verification systems that handle complex, multi-hop reasoning. Existing approaches have critical limitations: single-agent systems exhibit confirmation bias, while conventional multi-agent frameworks rely on homogeneous agents that exhibit groupthink, limiting critical evaluation. We present CARP (Claim Verification with Adversarial Reasoning and Planning), a novel multi-agent claim verification framework that organizes heterogeneous agents powered by multiple different language models competing as support and refutation teams. This adversarial structure forces comprehensive evaluation from both perspectives while mitigating confirmation bias and groupthink. Our framework incorporates systematic claim decomposition, strategic verification planning, and multi-hop knowledge retrieval to handle complex reasoning tasks. We evaluate CARP on two claim verification datasets–HOVER and FEVEROUS–where it demonstrates significant improvements in verification accuracy compared to existing single-agent and homogeneous multi-agent approaches, particularly for complex claims requiring multi-hop reasoning and evidence synthesis. Ablation studies confirm that both adversarial evaluation model and multihop knowledge retrieval contribute substantially to performance, with benefits scaling with reasoning complexity.
Coordinated Inauthentic Behavior on TikTok: Challenges and Opportunities for Detection in a Video-First Ecosystem
Detecting coordinated inauthentic behavior (CIB) is central to the study of online influence operations. However, most methods focus on text-centric platforms, leaving video-first ecosystems like TikTok largely unexplored. To address this gap, we develop and evaluate a computational framework for detecting CIB on TikTok, leveraging a network-based approach adapted to the platform’s unique content and interaction structures. Building on existing approaches, we construct user similarity networks based on shared behaviors, including synchronized posting, repeated use of similar speech segments, and multimedia content reuse, and apply graph pruning techniques to identify dense networks of likely coordinated accounts. Analyzing a dataset of 1.35M TikTok videos related to the 2024 U.S. Presidential Election, we uncover a range of coordinated activities, from synchronized amplification of political narratives and semi-automated content replication to AI-generated voiceovers and manufactured split-screen video formats. Our findings demonstrate that while several indicators proposed in this study effectively detect CIB on TikTok, other platform-native signals, such as video-based replies and Duet and Stitch interactions, often reflect organic engagement rather than inauthentic behavior, highlighting the platform’s distinct content dynamics and interaction mechanics. This work provides the first empirical foundation for studying CIB on TikTok, paving the way for future research into influence operations on video platforms.
Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
This study provides a comprehensive benchmark of Large Language Model (LLM) adaptation paradigms, specifically Fine-Tuning (FT) vs. In-Context Learning (ICL), for the detection of hyperpartisan news, fake news, political bias, and harmful content. We evaluate encoder-only and decoder-only architectures across 10 datasets in five languages: English, Spanish, Portuguese, Arabic, and Bulgarian. Our analysis covers a wide spectrum of ICL strategies, including zero-shot prompts, rule-based codebooks, Chain-of-Thought (CoT), and few-shot selection using both random and diversity-optimized (Determinantal Point Process) exemplars. Experimental results reveal that FT consistently outperforms ICL; notably, smaller fine-tuned models often surpass the performance of larger models (e.g., Llama-3.1-8B, Mistral-Nemo, and Qwen2.5-7B) used in ICL settings. We further find that model architecture suitability is task-dependent: fine-tuned decoders excel at political bias and fake news detection, while encoders remain superior for hyperpartisan and harmful tweet classification. Among ICL methods, the codebook approach generally yields the highest accuracy, frequently outperforming CoT. Our findings underscore that despite the versatility of LLMs, task-specific fine-tuning remains the most effective strategy for identifying nuanced problematic content online.
Cross-Platform and Cross-Lingual Dynamics of Wikipedia Sharing and Contribution
Wikipedia is a key knowledge source, but the literature suggests that attention from social media platforms rarely leads to edits. This research examines this relationship using a 15-year dataset of links from three major social media platforms (Twitter, Reddit, and Discord) that contains the link-sharing history of multiple languages and edits of Wikipedia articles. We first characterize the shared articles in terms of their language co-occurrence and editing history. Then, we apply a staggered difference-in-differences design with unshared language editions as controls. The present work reveals several distinctive platform-specific patterns. Reddit favors developed articles in a Eurocentric sphere, while Twitter is more dynamic and engages earlier. Our analysis also confirms the findings of prior work, finding that the association between sharing and editing activity is not strong. However, we also observed that high-intensity sharing correlates with measurable article growth. This study thus contributes to the literature by refining the one-way street view with comprehensive characteristics using multi-platform and multi-lingual analysis and showing that attention and contribution are conditionally connected.
Towards Detecting Persuasion on Social Media: From Model Development to Insights on Persuasion Strategies
Political advertising plays a pivotal role in shaping public opinion and influencing electoral outcomes, often through subtle persuasive techniques embedded in broader propaganda strategies. Detecting these persuasive elements is crucial for enhancing voter awareness and ensuring transparency in democratic processes. This paper presents an integrated approach that bridges model development and real-world application through two interconnected studies. First, we introduce a lightweight model for persuasive text detection that achieves state-of-the-art performance in Subtask 3 of SemEval 2023 Task 3 while requiring significantly fewer computational resources and training data than existing methods. Second, we demonstrate the model’s practical utility by collecting the Australian Federal Election 2022 Facebook Ads (APA22) dataset, partially annotating a subset for persuasion, and fine-tuning the model to adapt from mainstream news to social media content. We then apply the fine-tuned model to label the remainder of the APA22 dataset, revealing distinct patterns in how political campaigns leverage persuasion through different funding strategies, word choices, demographic targeting, and temporal shifts in persuasion intensity as election day approaches. Our findings not only underscore the necessity of domain-specific modeling for analyzing persuasion on social media but also show how uncovering these strategies can enhance transparency, inform voters, and promote accountability in digital campaigns.
Grok in the Wild: Characterizing the Roles and Uses of Large Language Models on Social Media
xAI’s large language model, Grok, is called by millions of people each week on the social media platform X. Prior work characterizing how large language models (LLMs) are used has focused on private, one-on-one interactions. Grok’s deployment on X represents a major departure from this setting, with interactions occurring in a public social space. In this paper, we systematically sample three months of interaction data to investigate how, when, and to what effect Grok is used on X. At the platform level, we find that Grok responds to 62% of requests, that the majority (51%) are in English, and that engagement is low, with half of Grok's responses receiving 20 or fewer views after 48 hours. We also inductively build a taxonomy of 10 roles that LLMs play in mediating social interactions and use these roles to analyze 41,735 interactions with Grok on X. We find that Grok most often serves as an information provider but, in contrast to LLM use in private one-on-one settings, also takes on roles related to dispute mediation, such as truth arbiter, advocate, and adversary. Finally, we characterize the population of users who prompted Grok and find that their self-expressed interests are closely related to the roles the model assumes in the corresponding interactions. Our findings provide an initial description of human-AI interactions on X, and a broader understanding of the diverse roles that LLMs may play in our online social spaces.
Reading Between the Lines: How Electronic Nonverbal Cues Shape Emotion Decoding
As text-based computer-mediated communication (CMC) increasingly structures everyday interaction, a central question re-emerges with new urgency: How do users reconstruct nonverbal expression in environments where embodied cues are absent? This paper provides the first systematic, theory-driven account of electronic nonverbal cues (eNVCs) - textual analogues of kinesics, vocalics, and paralinguistics - in public microblog communication. Across three complementary studies, we advance conceptual, empirical, and methodological contributions. Study 1 develops a unified taxonomy of eNVCs grounded in foundational nonverbal communication theory and introduces a scalable Python toolkit for their automated detection. Study 2, a within-subject survey experiment, offers the first causal evidence that eNVCs substantially improve emotional decoding accuracy and lower perceived ambiguity, while also identifying boundary conditions, such as sarcasm, under which these benefits weaken or disappear. Study 3, through focus group discussions, reveals the interpretive strategies users employ when reasoning about digital prosody, including drawing meaning from the absence of expected cues and defaulting toward negative interpretations in ambiguous contexts. Together, these studies establish eNVCs as a coherent and measurable class of digital behaviors, refine theoretical accounts of cue richness and interpretive effort, and provide practical tools for affective computing, user modeling, and emotion-aware interface design.
Leader-driven or Leaderless: How Participation Structure Sustains Engagement and Shapes Narratives in Online Hate Communities
Extremist communities increasingly rely on social media to sustain and amplify divisive discourse. However, the relationship between their internal participation structures, audience engagement, and narrative expression remains underexplored. This study analyzes ten years of Facebook activity by hate groups related to the Israel–Palestine conflict, focusing on anti-Semitic and Islamophobic ideologies. Consistent with prior work, we find that higher participation centralization in online hate groups is associated with greater user engagement across hate ideologies, suggesting the role of key actors in sustaining group activity over time. Meanwhile, our narrative frame detection models—based on an eight-frame extremist taxonomy (e.g., dehumanization, violence justification)—reveal a clear contrast across hate ideologies: centralized Islamophobic groups employ more uniform messaging, while centralized anti-Semitic groups demonstrate greater framing diversity and topical breadth, potentially reflecting distinct historical trajectories and leader coordination patterns. Analysis of the inter-group network indicates that, although centralization and homophily are not clearly linked, ideological distinctions emerge: Islamophobic groups cluster tightly, whereas anti-Semitic groups remain more evenly connected. Overall, these findings clarify how participation structure may shape the dissemination pattern and resonance of extremist narratives online and provide a foundation for tailored strategies to disrupt or mitigate such discourse.
Large-Scale Multimodal Content Analysis and Annotation with Vision-Language Models
Most social media research still treats content as text, even though images and videos carry a large share of what people actually see and share. This gap is especially serious for WhatsApp in India, where political communication often travels as posters, memes, screenshots, short clips, and forwarded videos in Hindi and other low-resource languages. Text-only analysis can therefore miss a large fraction of political content and distort conclusions about which topics spread and which become viral. We present a large-scale multimodal analysis of WhatsApp data collected via data donation across roughly 100 locations in India during the 2024 Indian General Elections. Using recent vision-language models (VLMs) and large language models (LLMs), we build a multimodal processing toolkit that represents text, images, and videos in a shared framework. We use this toolkit to produce three results. First, we map topic prevalence and diffusion across modalities and show that topic distributions differ sharply by modality: key domains such as politics are disproportionately non-textual, so text-only pipelines systematically undercount them. Second, we evaluate whether LLM/VLM-based classifiers can replace human annotation for socially sensitive labels. Models perform well for broad categories such as political and news content, but they are substantially less reliable for misinformation, hate speech, and caste-coded hostility, with failures driven by class imbalance, implicit meaning, and cultural context. Third, we connect WhatsApp content to mainstream narratives by building a reference set from YouTube uploads of prime-time television news over the same period and measuring multimodal narrative overlap under format transformation (e.g., headlines as screenshots and segments as clips). The narratives that align with mainstream coverage are carried largely by non-text WhatsApp messages and are far more likely to be viral than baseline content. Together, these findings show that multimodal methods are necessary for valid measurement of political communication on encrypted platforms, and they provide an auditable toolkit for studying diffusion, harms, and cross-channel narrative dynamics in low-resource settings.
From Posts to Progress: Associations Between Community Practices and Weight Loss Success
Online communities play a valuable role in providing social support and shaping health behaviors. In the context of personal health, social support, accountability, and consistency are key factors for success. Prior work has examined content and engagement in online health-related communities, but less is known about how moderation and community practices translate into measurable health outcomes. In this study, we investigate how moderation practices are related to user weight loss analyzing several thousands of users, over six years, in Reddit's largest weight loss community r/loseit. We find that user posting activity in specific moderator-created threads is associated with a stable and reliable weight loss. Particularly, structured and recurring moderating practices, especially those focused on progress updates are associated with greater weight loss compared to other threads. Our results demonstrate how moderation within online communities can actively support weight loss facilitating health behavioral change. Moreover, we illustrate how large-scale social media data can aid the design of digital health interventions.
Motivational Framing in Networked Publics on Indian Social Media: A Case Study of #HindusUnderAttack
Social movements employ various strategies to disseminate information, build support for their cause, and mobilize masses to take specific actions. One key tactical tool is framing. We utilize grounded theory to identify narrative, motivational, and issue-specific topical frames used in a digital networked public propagated by Hindu right-wing groups using #HindusUnderAttack on Twitter. We leverage pre-trained, finetuned RoBERTa models to identify the occurrence of each frame in the corpus. Relying on past works on the Hindutva movement, we build three hypotheses rooted in the use of motivational frames (i.e. calls to action). Our findings illustrate three critical insights into framing around #HindusUnderAttack: 1) politically aligned users deployed the motivational frame more frequently, 2) tweets about Bangladesh are more likely to contain motivational framing, and 3) motivational framing is associated with higher audience engagement through retweets. We also report important patterns of frame combinations. Finally, we situate our findings in the political economy of the region and extant literature of digital publics around the world.
Implied Authenticity Effect? The Impact of Explicit Labels on AI-Generated Content
This study investigates how labeling AI-generated content (AIGC) influences users’ perceptions of authenticity in social media environments. Motivated by emerging global regulations mandating the disclosure of AI-generated media, we designed a pre registered survey experiment to test two main questions: (1) Do AI labels reduce the perceived authenticity of AI-generated images? (2) Does exposure to labeled content affect perceived authenticity in unlabeled images (a potential spillover effect)? The pre-analysis plan and materials are available on OSF (see link below). We conducted a survey experiment with a German sample (N = 877) in which participants were randomly assigned to one of three groups: a control group without labels, a process-based label group (“AI-generated”), or a harm-based label group (“Misleading”). Participants viewed twelve Instagram-style posts, six of which contained AI-generated or AI-altered content. Perceived authenticity was measured by asking whether the depicted events actually occurred. Our results show that both labeling strategies significantly reduced perceived authenticity of AI-generated images, with average reductions of about 0.27 standard deviations. We also find evidence of an implied authenticity effect: exposure to labeled content slightly increased perceived authenticity in unlabeled images (about one-fifth the size of the direct labeling effect). Exploratory analyses indicate that internet skills and age are associated with perceived authenticity differences. By embedding labels in Instagram-style posts, our study increases ecological validity compared to earlier work focused on headlines or generic stimuli. Situated in Germany, a frontrunner in digital platform regulation, the findings provide rare non-U.S. evidence on AI labeling and contribute directly to ongoing policy debates.
Large Scale Narrative Analysis of Multimodal Memes
Current computational approaches to meme analysis primarily focus on individual memes, with an emphasis on tasks such as hateful content detection and sentiment analysis. In contrast, corpus-level analyses, which are necessary to reveal in-depth thematic narratives embedded in meme corpora, have remained within the purview of social science research. While qualitative methods such as inductive content analysis provide deeper insights, they are labor-intensive and lack scalability. To address this gap, we introduce MemeTopicTrees (MemeTT), a zero-shot pipeline that clusters multimodal memes based on their targets, aspects, sentiments, and opinions. In addition to clustering, MemeTT generates a descriptive narrative for each cluster to provide a nuanced understanding of meme corpora. By integrating multimodal aspect-based sentiment analysis with hierarchical clustering, MemeTT automates both semantic analysis and narrative generation at scale. Evaluated on a combined pool of three datasets spanning political, public health, and defense domains, MemeTT successfully produces fine-grained clusters and coherent narratives, with narrative relevance most pronounced at the target and aspect levels. Evaluations show that the best-performing models are highly accurate at identifying meme targets and aspects, although maintaining high accuracy for opinions and sentiments remains challenging. Furthermore, while cluster distinctiveness is robust at the target level, room for improvement remains at lower clustering levels. Despite these challenges, this approach offers a scalable solution for analyzing public sentiment and discourse in meme corpora. We lay the foundation for future research on the understudied task of automated meme corpus analysis.
Anatomy of an Election from a Gender Perspective
This paper investigates how gender shapes online political communication during an electoral campaign by analyzing the 2025 Canadian federal election. Drawing on a novel dataset of 193,620 social media posts from 921 candidates, we employ transformer-based topic modeling and a linguistic gender axis to quantify communicative patterns. Our analysis reveals three key findings. First, a statistically robust stylistic difference exists between the discourse of male and female candidates. Second, this gendered linguistic gap is not merely an artifact of ideology; it persists systematically across all major political parties, demonstrating its robustness across partisan contexts. Third, thematic analysis shows that women more frequently employ community-action framing across a range of policy-related discussions, while men place greater emphasis on economic and industrial issues during the electoral period. While the magnitudes of these effects are shaped by Canada's electoral environment, the mechanisms we identify offer a transferable framework for measuring gendered discourse in other political contexts. By documenting these patterns at scale, this study provides a high-resolution portrait of gender's persistent role in contemporary political communication and a replicable methodological toolkit for its study.
Framing Unionization on Facebook: Communication Around Representation Elections in the United States
Digital media have become central to how labor unions communicate, organize, and sustain collective action. Yet little is known about how unions' online discourse relates to concrete outcomes such as representation elections. This study addresses the gap by combining National Labor Relations Board (NLRB) election data with 158k Facebook posts published by U.S. labor unions between 2015 and 2024. We focused on five discourse frames widely recognized in labor and social movement communication research: diagnostic (identifying problems), prognostic (proposing solutions), motivational (mobilizing action), community (emphasizing solidarity), and engagement (promoting social media interaction). Using a fine-tuned RoBERTa classifier, we systematically annotated unions' posts and analyzed patterns of frame usage around election events. Our findings showed that diagnostic and community frames dominated union communication overall, but that frame usage varied substantially across organizations. Greater use of diagnostic, prognostic, and community frames prior to an election was associated with higher odds of a successful outcome. After elections, framing patterns diverged depending on results: after wins, the use of prognostic and motivational frames decreased, whereas after losses, the use of prognostic and engagement frames increased. By examining variation in message-level framing, the study highlights how communication strategies correlate with organizational success, contributing open tools and data, and complementing prior research in understanding digital communication of unions and social movements.
Reddit After Roe: A Computational Analysis of Abortion Narratives and Barriers in the Wake of Dobbs
The 2022 U.S. Supreme Court decision in Dobbs v. Jackson Women’s Health Organization reshaped the reproductive rights landscape, introducing new uncertainty and barriers to abortion access. We present a large-scale computational analysis of abortion discourse on Reddit, examining how barriers to access are articulated across information-seeking and information-sharing behaviors, different stages of abortion (before, during, after), and three phases of the Dobbs decision in 2022. Drawing on more than 17,000 posts from four abortion-related subreddits, we employed a multi-step pipeline to classify posts by information type, abortion stage, barrier category, and expressed emotions. Using a codebook of eight barrier types, including legal, financial, emotional, and social obstacles, we analyzed their associations with emotions and information behaviors. Topic modeling of model-generated barrier rationales further revealed how discourse evolved in response to shifting legal and cultural contexts. Our findings show that emotional and psychological barriers consistently dominate abortion narratives online, with emotions such as nervousness, confusion, fear, and sadness prevalent across discourse. By linking information behaviors, barriers, emotions, and temporal dynamics, this study provides a multi-dimensional account of how abortion is navigated in online communities.
Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection Using Debias Tuning In Large Language Models
Commercial Large Language Models (LLMs) have recently incorporated memory features to deliver personalised responses. This memory retains details such as user demographics and individual characteristics, allowing LLMs to adjust their behaviour based on personal information. However, the impact of integrating personalised information into the context has not been thoroughly assessed, leading to questions about its influence on LLM behaviour. Personalisation can be challenging, particularly with sensitive topics. In this paper, we examine various state-of-the-art LLMs to understand their behaviour in different personalisation scenarios, specifically focusing on hate speech. We prompt the models to assume country-specific personas and use different languages for hate speech detection. Our findings reveal that context personalisation significantly influences LLMs' responses in this sensitive area. To mitigate these unwanted biases, we fine-tune the LLMs by penalising inconsistent hate speech classifications made with and without country or language-specific context. The refined models demonstrate improved performance in both personalised contexts and when no context is provided.
Images Amplify Misinformation Sharing in Vision-Language Models
As language and vision–language models (VLMs) become central to information access and online interaction, concerns grow about their potential to amplify misinformation. Human studies show that images boost the perceived credibility and shareability of information, raising the question of whether VLMs exhibit the same vulnerability. We present the first study examining how images influence VLMs' propensity to reshare news content, how this effect varies across model families, and how persona conditioning and content attributes modulate such behavior. We develop a jailbreaking-inspired prompting strategy that bypasses VLMs' default refusals to engage with controversial news, allowing them to generate resharing decisions across diverse topics and elicited traits, including antisocial ones. We evaluate four state-of-the-art VLMs on a novel multimodal dataset of fact-checked political news from PolitiFact, paired with images and ground-truth veracity labels. Our experiments show that image presence increases resharing rates by 14.5% for false news and 5.3% for true news. Persona conditioning further modulates this effect: Dark Triad traits amplify resharing of false news, whereas Republican-aligned profiles reduce sensitivity to veracity. Among the tested models, Claude-3-Haiku demonstrates the greatest robustness to visual misinformation. These findings reveal that VLMs replicate human-like biases in response to images, underscoring emerging risks for multimodal AI systems. They point to the need for evaluation frameworks and mitigation strategies that account for visual influence and persona-driven variability, particularly in sociotechnical settings where AI systems shape public discourse and information sharing.
A Nonpartisan Study of Deepfake Activity and Engagement Around the 2024 US Presidential Election
We present the first quantitative study of deepfake activity during the 2024 U.S. presidential election by analyzing a novel dataset of 231 deepfakes (images, videos, and audio clips) across social media during the May-December 2024 election period. Our comprehensive statistical analysis examines five research questions: (1) whether deepfake publication spikes occur around key election events (KEEs), (2) whether there is a temporal relationship between deepfakes and KEEs (before, during, or after), (3) which specific types of KEEs trigger deepfake spikes, (4) whether KEEs boost engagement with deepfakes, and (5) the differential impact of various KEEs on deepfake engagement. Our findings reveal that spikes in deepfake activity preceded KEEs, and engagement with deepfakes (e.g., likes, comments) surged during pre-KEE time windows. Our curated dataset offers researchers a valuable resource to study the impact of synthetic media in political contexts, while our findings provide valuable advice for policymakers and social platforms to develop appropriate measures to counter potential malign deepfakes before future elections.
Social and Political Framing in Search Engine Results
Search engines play a crucial role in shaping public discourse by influencing how information is accessed and framed. While prior research has extensively examined various dimensions of search bias---such as content prioritization, indexical bias, political polarization, and sources of bias---an important question remains underexplored: how do search engines and ideologically-motivated user queries contribute to bias in search results. This study analyzes the outputs of major search engines using a dataset of politically and socially contentious topics. The findings reveal that search engines not only prioritize content in ways that reflect underlying biases but also that ideologically-driven user queries exacerbate these biases, resulting in the amplification of specific narratives. Moreover, significant differences were observed across search engines in terms of the sources they prioritize. These results suggest that search engines may play a pivotal role in shaping public perceptions by reinforcing ideological divides, thereby contributing to the broader issue of information polarization.
LLM-Powered Analysis of IoT User Reviews: Tracking and Ranking Security and Privacy Concerns
Being able to understand the security and privacy (S&P) concerns of IoT users brings benefits to both developers and users. To learn about users' views, we examine Amazon IoT reviews - one of the biggest IoT markets. This work presents a state-of-the-art methodology to identify and categorize reviews in which users express S&P concerns. We developed an automated pipeline by fine-tuning GPT-3.5-Turbo to build two models: the Classifier-Rationalizer-Categorizer and the Thematic Mapper. By leveraging dynamic few-shot prompting and the model's large context size, our pipeline achieved over 97% precision and recall, significantly outperforming keyword-based and classical ML methods. We applied our pipeline to 91K Amazon reviews about fitness trackers, smart speakers and cameras, over multiple years. We found that on average 5% contained S&P concerns, while security camera exhibited the highest prevalence at 10%. Our method detected significantly more S&P-relevant reviews than prior works: 15x more for fitness trackers, 29% more for smart speakers, and 70% more for cameras. Our longitudinal analysis reveals that concerns like surveillance and data control have persisted for years, suggesting limited industry progress. We demonstrate that across all device types, users consistently demand more precise control over what data is collected and shared. We uncover challenges in multi-user and multi-device interactions, identifying two previously unreported themes concerning inadequate controls for account separation and data access. These findings, ranging from broad persistent trends to specific instances of customer loss, offer actionable insights for developers to improve user satisfaction and trust.
Shifting Gatekeeping Practices and Audience Engagement Under AI-Assisted Comment Moderation
Traditionally, The New York Times has maintained substantial gatekeeping control over audience contributions on its website by selectively enabling comment sections on certain articles and pre-moderating comments. This study explores changes to these gatekeeping practices and subsequent audience engagement following the transition to AI-assisted comment moderation. The introduction of AI-assisted comment moderation coincided with an increase in articles with comment sections. However, we found that this increase was less pronounced for contentious, social issue topics than for soft news sections. Comparing comment quality before and after AI assistance across eight dimensions such as coherence and substantiveness, we observed some section-wise differences, although overall, the differences were minimal. We also found that AI-assisted moderation was associated with an increase in engagement among prolific commenters and an increase in the proportion of moderately active commenters. Overall, these findings indicate that AI-assisted moderation is associated with changes in gatekeeping and increased audience participation without compromising on the aggregate quality of comments published.
Pay to Win: Copyright Infringement and Academic Integrity Violation in Crowdsourced Document-Sharing Ecosystems
Recently, crowdsourced academic document-sharing websites like Course Hero have been arousing controversy among media, students, and faculty. In this paper, we investigate the growth of these platforms and how they infringe on the copyright of university educators and aid in academic integrity violations of students. We collected and analyzed the metadata of over 227,000 documents shared on three popular platforms, including Course Hero, Docsity, and StuDocu, and found that Course Hero was the largest. Focusing on Course Hero, we then conducted two user studies (n=56) and (n=153) with university educators. The first study analyzed Course Hero's impact on copyright and academic integrity violations. The second study examined the motivation behind faculty affiliation with Course Hero as "Verified Educators." Our results show that Course Hero has been growing exponentially since its inception. The results also highlight educators' general opposition to students' use of these platforms to gain an unfair advantage in coursework while acknowledging some alternative legitimate uses of these platforms. We show that educators create their own profiles on these platforms to identify copyrighted materials or initiate document takedown requests. However, educators do not always have copyright claims on concerning documents, and the takedown process is considered overwhelming. We conclude by discussing the implications of our findings and directions for future research.
Learning Inflation Narratives from Reddit: How Lightweight LLMs Reveal Forward-Looking Economic Signals
Public perceptions and expectations of inflation shape household spending, wage bargaining, and policy support, making them key determinants of macroeconomic outcomes. However, current measures rely on infrequent surveys and offer limited insight into underlying narratives and sector-specific concerns. This paper presents a novel approach to measuring public perception of inflation, using lightweight large language models (LLMs) fine-tuned on domain-specific Reddit data. We created an inflation classifier trained on posts related to components of the U.S. Consumer Price Index (CPI). When applied to more than 10 years of Reddit discussions (2012–2022), this classifier produces monthly Reddit inflation scores (RIS), which we validated against actual economic indicators. Our results show that fine-tuned lightweight LLMs perform well even with smaller training datasets, and the Reddit inflation scores strongly correlate with CPI (r = 0.91) and closely align with the University of Michigan: Inflation Expectation (MICH). Importantly, Granger causality tests suggested that social media–based inflation scores often precede movements in both CPI and MICH, indicating their potential as predictive, forward-looking economic signals. Furthermore, change-point and lexical analyses uncovered shifts in inflation-related narratives across sectors like groceries, transportation, and housing, revealing dimensions of inflation concern that are not directly observable in aggregate price indices. By complementing traditional economic indicators with narrative-rich signals, this study demonstrates how NLP-based measures can facilitate earlier detection of inflationary pressures and policy responses.
Asking for It: Question-Answering for Predicting Rule Infractions in Online Content Moderation
Online communities rely on a mix of platform policies and community-authored rules to define acceptable behavior and maintain order. However, these rules vary widely across communities, evolve over time, and are enforced inconsistently—posing challenges for transparency, governance, and automation. In this paper, we model the relationship between rules and their enforcement at scale, introducing ModQ, a novel question-answering framework for rule-sensitive content moderation. Unlike prior classification or generation-based approaches, ModQ conditions on the full set of community rules at inference time and identifies which rule best applies to a given comment. We implement two model variants—extractive and multiple-choice QA—and train them on large-scale datasets from Reddit and Lemmy, the latter of which we construct from publicly available moderation logs and rule descriptions. Both models outperform state-of-the-art baselines in identifying moderation-relevant rule violations, while remaining lightweight and interpretable. Notably, ModQ models generalize effectively to unseen communities and rules, supporting low-resource moderation settings and dynamic governance environments.
Ideology Prediction of German Political Texts
Elections represent a crucial milestone in a nation's ongoing development. To better understand the political rhetoric from various movements, ranging from left to right, we propose a transformer-based model capable of projecting the political orientation of a text on a continuous left-to-right spectrum, represented by a normalized scalar, d ∈ [−1, 1]. This approach enables analysts to focus on specific segments of the political landscape, such as conservatives, while excluding liberal and far-right movements. Such a task can only be achieved with multiclass classifiers, provided that the desired orientation is incorporated within one of their predefined classes. To determine the most suitable foundation model among 13 candidate transformers for this task, we constructed four distinct corpora. One corpus comprised annotated plenary notes from the German Bundestag, while another was based on an official online decision-making tool, Wahl-O-Mat. The third corpus consisted of articles from 33 newspapers, each identified by its political orientation, and the fourth included 535,200 tweets from 597 members of the 20th and 21st German Bundestag. To mitigate overfitting, we used two distinct corpora for training and two for testing, respectively. For in-domain performance, DeBERTa-large achieved the highest F1 score (F1 = 0.844) as well as for the X (Twitter) out-of-domain test (ACC = 0.864). Regarding the newspaper out-of-domain test, Gemma2-2B excelled (MAE = 0.172). This study demonstrates that transformer models can recognize political framing in German news at the level of public opinion polls. Our findings suggest that both the model architecture and the availability of domain-specific training data can be as influential as model size for estimating political bias. We discuss methodological limitations and outline directions for improving the robustness of bias measurement.
Racial Biases in Social Media Discussions About Soccer Players
Recent reports suggest that Black soccer players are often subjected to racial abuse from fans in social media commentary. This study develops a pipeline to collect data from Reddit and Twitter during a four-season period spanning three soccer tournaments, linking posts with mentioned soccer players to investigate the prevalence of racial bias in these posts. Our results highlight subtle forms of racial disparity in social media comments about soccer players. Specifically, we find that Black players tend to receive more toxic comments than White players and that Black players receive more comments related to their physical attributes, in line with previous findings in traditional media coverage. We hope that our research and multiplatform social media datasets help raise awareness of subtle forms of racial disparities in the discussion of athletes and motivate further research to characterize racial bias in soccer.
From Meetups to Adminship: The Offline Path to Leadership on Wikipedia
Wikipedia is one of the most widely used platforms for online knowledge production, yet its sustainability depends on a small group of elected administrators who enforce rules. Prior research has largely explained leadership trajectories through online activity and platform-native measures. In this paper, I extend this perspective by examining how offline interactions are associated with leadership outcomes in the German-language Wikipedia. Using a unique, observational dataset that combines two decades of online behavioural traces with records of offline community events, I apply hybrid multilevel random effects models to analyse both candidacy and election success. I find that attending offline meetings does not strongly predict who decides to run for administrator, but it substantially increases the chances of being elected. These statistical effects, however, are not consistent over time: offline interactions were most influential in the platform’s early years and declined in importance in more recent periods. The results demonstrate how social ties and in-person interactions intersect with online governance processes, offering new insights into leadership, participation, and the temporal dynamics of peer production communities. Beyond Wikipedia, this work contributes to computational social science by showing how online and offline interactional layers jointly structure leadership in large-scale digital platforms, while highlighting the need for future work capable of directly testing the mechanisms proposed here.
Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts
Bridging content that brings together individuals with opposing viewpoints on social media remains elusive, overshadowed by echo chambers and toxic exchanges. We propose that algorithmic curation could surface such content by considering constructive conflicts as a foundational criterion. We operationalize this criterion through controversiality to identify challenging dialogues and toxicity resilience to capture respectful conversations. We develop high-accuracy models to capture these dimensions. Analyses based on these models demonstrate that assessing resilience to toxic responses is not the same as identifying low-toxicity posts. We also find that political posts are often controversial and tend to attract more toxic responses. However, some posts, even the political ones, are resilient to toxicity despite being highly controversial, potentially sparking civil engagement. Toxicity resilient posts tend to use politeness cues, such as showing gratitude and hedging. These findings suggest the potential for framing the tone of posts to encourage constructive political discussions.
Identity Emergence in the Context of Vaccine Criticism in France
This study investigates the emergence of collective identity among individuals critical of vaccination policies in France during the COVID-19 pandemic. As concerns grew over mandated health measures, a loose collective formed on Twitter to assert autonomy over vaccination decisions. Using analyses of pronoun usage, outgroup labeling, and tweet similarity, we examine how this identity emerged. A turning point occurred following President Macron's announcement of mandatory vaccination for health workers and the health pass, sparking substantial changes in linguistic patterns. We observed a shift from first-person singular (I) to first-person plural (we) pronouns, alongside an increased focus on vaccinated individuals as a central outgroup, in addition to the media and President Macron. This shift in language patterns was further reflected in the behavior of new users. An analysis of incoming users revealed that a core group of frequent posters played a crucial role in fostering cohesion and shaping norms. New users who joined during the week of Macron's announcement and continued posting afterward showed an increased similarity with the language of the core group, contributing to the crystallization of the emerging collective identity. By leveraging large-scale social media data and computational methods, we provide insights into the mechanisms through which resistance movements solidify their identity online in response to policy changes.
Mind the Gap: Pitfalls of LLM Alignment with Asian Public Opinion
Large Language Models (LLMs) are increasingly being deployed in multilingual, multicultural settings, yet their reliance on predominantly English-centric training data risks misalignment with the diverse cultural values of different societies. In this paper, we present a comprehensive, multilingual audit of the cultural alignment of contemporary LLMs including GPT-4o-Mini, Gemini-2.5-Flash, Llama 3.2, Mistral and Gemma 3 across India, East Asia and Southeast Asia. Our study specifically focuses on the sensitive domain of religion as the prism for broader alignment. To facilitate this, we conduct a multi-faceted analysis of every LLM's internal representations, using log-probs/logits, to compare the model's opinion distributions against ground-truth public attitudes. We find that while the popular models generally align with public opinion on broad social issues, they consistently fail to accurately represent religious viewpoints, especially those of minority groups, often amplifying negative stereotypes. Lightweight interventions, such as demographic priming and native language prompting, partially mitigate but do not eliminate these cultural gaps. We further show that downstream evaluations on bias benchmarks (such as CrowS-Pairs, IndiBias, ThaiCLI, KoBBQ) reveal persistent harms and under-representation in sensitive contexts. Our findings underscore the urgent need for systematic, regionally grounded audits to ensure equitable global deployment of LLMs.
Clustering Internet Memes with Metric Learning and Dynamic Modality Weighting
This paper presents a two-stage metric learning approach for large-scale clustering of internet memes into pre-defined knowledge categories. We also introduce a novel dynamic modality weighting step to adaptively balance the influence of image and text attributes, which is previously unexplored in meme similarity tasks, and outperforms multimodal models such as CLIP. We train and evaluate the pipeline across 678,734 memes from KnowYourMeme.com and achieve an F1 score of 91% when assigning unseen memes from a different source to KnowYourMeme.com categories. Our proposed approach incorporates more meme types than prior research, enabling the alignment of individual memes to crucial knowledge sources for information retrieval tasks, with further applications in meme analysis, misinformation detection, hateful meme detection and internet cultural studies.
Exploring COVID-19 Framing Across Diverse Platforms: Analyzing Semantic and Contextual Shifts in Public Discussion, News Media, and Government Communication
Understanding how shared issues are framed differently across public, news, and government discourse is central to the study of COVID-19 communication. This paper uses the previously validated Linked Latent Theta Role (LLTR) model as part of a novel analytic technique to examine framing differences across Reddit posts, mainstream news articles, and state public health bulletins. Rather than introducing a new model, we operationalize LLTR outputs to compare cross-source framing by examining how shared topic words are embedded in different syntactic constructions. Using a source-balanced corpus, we measure cross-source differences using Jensen–Shannon divergence over distributions of dependency-based relation–argument pairs, and contrast these results with a lexical baseline. Across eight COVID-19 topics, we find that sources often rely on overlapping topic vocabularies, yet diverge substantially in their syntactic realizations of those topics. Inspection of high-divergence grammatical evidence reveals systematic differences in how sources assign agency, attribute responsibility, and structure evaluative context around shared topical concepts. These findings suggest that grammar-aware representations provide an interpretable and scalable basis for identifying framing differences that are not visible at the lexical level alone.
Fluent but Unfeeling: The Emotional Blind Spots of Language Models
The versatility of Large Language Models (LLMs) in natural language understanding has made them increasingly popular in mental health research. While many studies explore LLMs' capabilities in emotion recognition, a critical gap remains in evaluating whether LLMs align with human emotions at a fine-grained level. Existing research typically focuses on classifying emotions into predefined, limited categories, overlooking more nuanced expressions. To address this gap, we introduce EXPRESS, a benchmark dataset curated from Reddit communities featuring 251 fine-grained, self-disclosed emotion labels. Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion theories, enabling a fine-grained comparison. Systematic testing of prevalent LLMs under various prompt settings reveals that accurately predicting emotions that align with human self-disclosed emotions remains challenging. Qualitative analysis further shows that while certain LLMs generate emotion terms consistent with established emotion theories and definitions, they sometimes fail to capture contextual cues as effectively as human self-disclosures. These findings highlight the limitations of LLMs in fine-grained emotion alignment and offer insights for future research aimed at enhancing their contextual understanding.
Inconsistencies in Classification of Online News Articles: A Call for Common Standards in Brand Safety Services
This study examines inconsistencies in the brand safety classifications of online news articles by analyzing ratings from three leading brand safety providers, DoubleVerify, Integral Ad Science, and Oracle. We focus on news content because of its central role in public discourse and the significant financial consequences of unsafe classifications in a sector that is already underserved by digital ad spending. By collecting data from 4,352 news articles on 51 domains, our analysis shows that brand safety services often produce conflicting classifications, with significant discrepancies between providers. These inconsistencies can have harmful consequences for both advertisers and publishers, leading to misplaced advertising spending and revenue losses. This research provides critical insights into the shortcomings of the current brand safety landscape. We argue for a standardized and transparent brand safety system to mitigate the harmful effects of the current system on the digital advertising ecosystem.
Mapping the Political Discourse in the Brazilian Chamber of Deputies: A Multi-Faceted Computational Approach
Analyses of legislative behavior often rely on voting records, overlooking the rich semantic and rhetorical content of political speech. In this paper, we ask three complementary questions about parliamentary discourse: how things are said, what is being said, and who is speaking in discursively similar ways. To answer these questions, we introduce a scalable and generalizable computational framework that combines diachronic stylometric analysis, contextual topic modeling, and semantic clustering of deputies’ speeches. We apply this framework to a large-scale case study of the Brazilian Chamber of Deputies, using a corpus of over 450,000 speeches from 2003 to 2025. Our results show a long-term stylistic shift toward shorter and more direct speeches, a legislative agenda that reorients sharply in response to national crises, and a granular map of discursive alignments in which regional and gender identities often prove more salient than formal party affiliation. More broadly, this work offers a robust methodology for analyzing parliamentary discourse as a multidimensional phenomenon that complements traditional vote-based approaches.
In Times of Crisis: An Exploratory Study of Media and Political Discourse on YouTube During the 2024 French Elections
YouTube has emerged as a major platform for political communication and news dissemination, particularly during high-stakes electoral periods. In the context of the 2024 European Parliament and French legislative elections, this study investigates how political actors and news media used YouTube to shape public discourse. We analyze over 100,000 video transcripts and metadata from 74 French YouTube channels operated by national news outlets, local media, and political figures. To identify the key themes emphasized during the campaign period, we applied a semi-automated method that combined large language models with clustering and manual review. The results reveal distinct thematic patterns across the political spectrum and media types, with right-leaning news outlets focusing on topics like immigration, while left-leaning emphasized protest and media freedom. Themes generating the most audience engagement, measured by comment-to-view ratios, were most often the most polarizing ones. In contrast, less polarizing themes such as video games and nature showed higher approval, reflected in like-to-view ratios. We also observed a general tendency across all media types to portray political figures in neutral or critical terms rather than favorable ones.
A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
In many real-world scenarios, an individual's local social network carries significant influence over the opinions they form and subsequently propagate. In this paper, we propose a novel diffusion model — the Pressure Threshold model (PT) — for dynamically simulating the spread of influence through a social network. This model extends the popular Linear Threshold (LT) model by adjusting a node's outgoing influence in proportion to the influence it receives from its activated neighbors. We examine the Influence Maximization (IM) problem under this framework, which involves selecting seed nodes that yield maximal graph coverage after a diffusion process, and describe how the problem manifests under the PT model. Experiments on real-world networks, supported by enhancements to the open-source network-diffusion library CyNetDiff, reveal that greedy IM under PT can yield seed sets distinct from those under LT. Furthermore, the analyses show that densely connected networks amplify pressure effects far more strongly than sparse networks.
Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube’s Influencer Economy
YouTube has evolved into a powerful platform where creators monetize their influence through affiliate marketing, raising concerns about transparency and ethics, especially when creators fail to disclose their affiliate relationships. Although regulatory agencies like the US Federal Trade Commission (FTC) have issued guidelines to address these issues, non-compliance and consumer harm persist, and the extent of these problems remains unclear. In this paper, we introduce tools, developed with insights from recent advances in Web measurement and NLP research, to examine the state of the affiliate marketing ecosystem on YouTube. We apply these tools to a 10-year dataset of 2 million videos from nearly 540,000 creators, analyzing the prevalence of affiliate marketing on YouTube and the rates of non-compliant behavior. Our findings reveal that affiliate links are widespread, yet disclosure compliance remains low, with most videos failing to meet FTC standards. Furthermore, we analyze the effects of different stakeholders in improving disclosure behavior. Our study suggests that the platform is highly associated with improved compliance through standardized disclosure features. We recommend that regulators and affiliate partners collaborate with platforms to enhance transparency, accountability, and trust in the influencer economy.
C2PA Provenance Labels Increase Trust in Digital News Platforms Across Western Countries
Misinformation and disinformation threaten global public trust in news media. Generative AI exacerbates mistrust by making it difficult to distinguish authentic images from AI-generated ones. This study examines whether accompanying images with C2PA (Coalition for Content Provenance and Authenticity) provenance labels can restore trust. C2PA is an open standard that cryptographically secures and describes a media file’s origin and editing history. We conducted an online experiment with N=6,114 participants, reflecting audiences of six major news sources in the US, UK, and Norway. Each participant evaluated six news article previews with images, either accompanied by a provenance label (three levels of detail) or not. Presenting provenance metadata to participants significantly improved their perceptions of an image’s transparency and credibility, and also increased feelings of trust in a presented news source. These results show that verifiable provenance makes visual content more inspectable and strengthens brand trust. By adopting C2PA or similar frameworks, news organizations can counter AI-generated disinformation and improve audience trust.
Telegram, initially a messaging app, has evolved into a platform where users can interact with various services through programmable applications, bots. Bots provide a wide range of uses, from moderating groups, helping with online shopping, to even executing trades in financial markets. However, Telegram has been increasingly associated with various illicit activities---financial scams, stolen data, non-consensual image sharing, among others, raising concerns bots may be facilitating these operations. This paper is the first to characterize Telegram bots at scale, through the following contributions. First, we offer the largest general-purpose message dataset and the first bot dataset. Through snowball sampling from two published datasets, we uncover over 67,000 additional channels, 492 million messages, and 32,000 bots. Second, we develop a system to automatically interact with bots in order to extract their functionality. Third, based on their description, chat responses, and the associated channels, we classify bots into several domains. Fourth, we investigate the communities each bot serves, by analyzing supported languages, usage patterns (e.g., duration, reuse), and network topology. While our analysis discovers useful applications such as crowdsourcing, we also identify malicious bots (e.g., used for financial scams, illicit underground services) serving as payment gateways, referral systems, and malicious AI endpoints. By exhorting the research community to look at bots as software infrastructure, this work hopes to foster further research useful to content moderators, and to help interventions against illicit activities.
Towards Transparent Stance Detection: A Zero-Shot Approach Using Implicit and Explicit Interpretability
Zero-Shot Stance Detection (ZSSD) identifies the attitude of the post toward unseen targets. Existing research using contrastive, meta-learning, or data augmentation suffers from generalizability issues or lack of coherence between text and target. Recent works leveraging large language models (LLMs) for ZSSD focus either on improving unseen target-specific knowledge or generating explanations for stance analysis. However, most of these works are limited by their over-reliance on explicit reasoning, provide coarse explanations that lack nuance, and do not explicitly model the reasoning process, making it difficult to interpret the model’s predictions. To address these issues, in our study, we develop a novel interpretable ZSSD framework, IRIS. We provide an interpretable understanding of the attitude of the input towards the target implicitly based on sequences within the text (implicit rationales) and explicitly based on linguistic measures (explicit rationales). Our IRIS approach considers stance detection as an information retrieval ranking task, understanding the relevance of implicit rationales for different stances to guide the model towards correct predictions without requiring the ground-truth of rationales, thus providing inherent interpretability. In addition, explicit rationales based on communicative features help decode the emotional and cognitive dimensions of stance, offering an interpretable understanding of the author's attitude towards the given target. Extensive experiments on the benchmark datasets of VAST, EZ-STANCE, P-Stance, and RFD using 50%, 30%, and even 10% training data prove the generalizability of our model, benefiting from the proposed architecture and interpretable design.
Quantifying the Illicit Ecosystem of Betting Apps in India
Online betting and gambling apps in India have expanded rapidly, alongside growing concern about financial loss, debt stress, and addictive use patterns. Yet the ecosystem is difficult to quantify because recruitment and harm are observed in different places: users are often acquired through social media promotion, while harms become visible later inside apps and in user complaints. We address this measurement gap with a mixed-method, multi-source study that links promotion to downstream experience. We compile three complementary datasets. First, we collect and analyze tens of thousands of betting-related advertisements from Meta’s Ad Library using an extensive keyword strategy to measure scale and characterize persuasive frames. Second, we gather a purposive sample of organic Instagram posts from ten betting-linked hashtags to study how similar narratives circulate outside formal advertising, including through surrogate sports pages and influencer-style content. Third, we analyze over 300,000 Google Play reviews for a set of betting apps, using topic modeling to extract recurring user-reported problems that reflect a harm surface including financial loss, withdrawal friction, and customer support failure. We connect these layers by constructing a shared narrative codebook for paid and organic promotion and mapping those recruitment narratives to review topics. Across sources, we find a consistent mismatch between what is promised at recruitment and what users report after adoption. Paid ads frequently frame betting as simple, quick, and highly winnable, while reviews repeatedly describe difficulty winning, blocked or delayed withdrawals, unclear rules, and perceived extractive design. Organic promotion often uses more coded and informal presentation than official ads, potentially reducing detectability while funneling users toward the same apps and referral pathways. Together, these results provide one of the first large-scale, cross-source measurements of India’s online betting promotion ecosystem and its associated user-reported harms, and they offer a general approach for studying how potentially harmful digital services sustain growth through mainstream platforms even under evolving regulation.
Dissecting Electric Vehicle Conversations on Twitter/X: A Tree-Based Analysis of Discussions
Electric vehicles (EVs) have become a focal point in public debates on climate change and transportation, particularly in Europe following the European Parliament's decision to ban the sale of new combustion engine vehicles by 2035. This study analyzes French conversations on EVs using a novel open-source dataset of Twitter discussion trees. The dataset includes over 1 million tweets, structured as 22,635 discussion trees, and offers insights into the dynamics of online discourse about EVs. We manually annotated a subset of tweets and fine-tuned a BERTweetFr model for automatic classification and sentiment analysis. Our findings reveal specific structural patterns in online discussions, varied behavior and influence of key actors, and thematic contexts in which EV-related topics are addressed. This research contributes to understanding public opinion on EVs and provides a methodological framework for analyzing online conversations.
Web2Wiki: Characterizing Wikipedia Linking Across the Web
Wikipedia is one of the most visited websites globally, yet its role beyond its own platform remains largely unexplored. In this paper, we present the first large-scale analysis of how Wikipedia is referenced across the Web. Using a dataset from Common Crawl, we identify over 90 million Wikipedia links spanning 1.68% of Web domains and examine their distribution, context, and function. Our analysis of English Wikipedia reveals four key findings: (1) The topics of Wikipedia articles referenced on the Web differ from those cited on Reddit and those prominent within Wikipedia’s own link structure, (2) Wikipedia is most frequently cited by news and science websites for informational purposes, while commercial websites reference it less often. (3) The majority of Wikipedia links appear within the main content rather than in boilerplate or user-generated sections, highlighting their role in structured knowledge presentation. (4) Most links (95%) serve as explanatory references rather than as evidence or attribution, reinforcing Wikipedia’s function as a background knowledge provider. While this study focuses on English Wikipedia, our publicly released WEB2WIKI dataset includes links from multiple language editions, supporting future research on Wikipedia’s global influence on the Web.
Forced Migration and Information Seeking Behavior on Wikipedia: Insights from the Ukrainian Refugee Crisis
Gathering information about where to migrate to is an important component of the migration process, particularly in the context of forced migration, when individuals must make rapid decisions under conditions of uncertainty. In this study, we investigate the relationship between forced migration and online information-seeking behavior on Wikipedia. Focusing on the 2022 Russian invasion of Ukraine, we analyze how the resulting refugee crisis, which led to over six million Ukrainians fleeing to countries across Europe, affected information-seeking behavior on Wikipedia, as measured by views of articles about European cities. We compared changes in the number of views of Ukrainian-language Wikipedia articles, used as a proxy for information-seeking by Ukrainians, with those in four other language editions of Wikipedia. Our results show that views of Ukrainian-language Wikipedia articles about European cities are more strongly correlated with the number of Ukrainian refugees who applied for temporary protection in European countries than views of articles in other languages. As Poland and Germany emerged as the main destinations of Ukrainian refugees, we investigated further and found that the number of applications for temporary protection in Polish and German cities is also more strongly correlated with views of their respective Wikipedia articles in Ukrainian. Finally, we analyzed the temporal relationship between refugee flows to Poland and information-seeking on Wikipedia. Our findings reveal that refugee border crossings preceded the increase in views of Ukrainian-language Wikipedia articles about Polish cities, suggesting that information-seeking surged after displacement. This highlights the reactive nature of information-seeking behavior during forced migration, in contrast to the pre-departure planning behavior that is characteristic of regular labor migration. However, while official applications for protection often lagged behind border crossings by at least a couple of weeks, Wikipedia activity increased almost immediately after border crossings. Thus, information-seeking on Wikipedia can offer a near real-time indicator of emerging migration patterns during crises.
Exploring YouTube Political Communication Networks During the 2024 French Elections
In 2024, France was shaken by the far-right National Rally's victory in the European elections. In response to this unprecedented result, French President Emmanuel Macron dissolved the National Assembly, triggering legislative elections just two weeks later. A whirlwind campaign followed, partly on social media, as is now the norm, and concluded with the victory of a left-wing coalition. This article examines the YouTube activity of two key actors during this period—news media and politicians—and the commenting behavior they generated. We built a dataset of 35 news media channels, 28 politicians & party channels, 43.5k videos posted from three months before the European elections to one week after the second round of the legislative elections, and 7.4M associated comments. We examined upload activity and engagement across political orientations and used network analysis methods to uncover the structure of their commenting communities. We also identified politicians’ appearances on news media channels and assessed their impact on commenting user bases. Our findings show that, among politicians and party channels, far-right and left-wing ones were significantly more active and received substantially higher engagement (views, likes, and comments) than other groups, with denser and more clustered commenting communities. About 7% of commenters commented across political orientations and were much more active than in-group commenters. News media channels tended to favor politically aligned guests, while centrist politicians were overrepresented. Finally, politicians presence in the videos of a specific news media channel increased the share of commenters who were active on this channel and political channels, regardless of their orientation.
The Failed Migration of Academic Twitter: A Case Study of Precocious Adopters
Following changes in Twitter's ownership in 2022 and subsequent changes to content moderation policies, many in academia looked to move their discourse elsewhere and migration to Mastodon was pursued by some. Our study examines the behavior of a self-organized group of early academic adopters who joined Mastodon following changes in Twitter’s ownership. Utilizing publicly available user account data drawn from a voluntarily curated list of academics, we track the posting activity of these early adopters on Mastodon over a one-year period. We also study follower-followee and interaction relationships to map internal networks, finding that the subset of academics who migrated to Mastodon were well-connected. However, this strong internal connectivity was insufficient to prevent users from returning to Twitter/X. Our analyses show that early adopters struggled to maintain engagement, shaped by Mastodon’s decentralized design and competition from alternatives such as Bluesky and Threads. The migration effort lost momentum after an initial surge, as most early adopters reduced activity or returned to Twitter. Our survival analysis further reveals that retention is strongly linked to diverse cross-server engagement and topic-specific server communities. Users with large pre-existing Twitter presence face significantly higher attrition risk, highlighting the challenge of replicating established social connections in a decentralized ecosystem. By examining the coordinated migration attempt of early adopters, we find that even this highly motivated group faced substantial challenges, suggesting that later or less coordinated efforts would likely encounter even greater barriers.
Grievance Politics vs. Policy Debates: A Cross-Platform Analysis of Conservative Discourse on Truth Social and Reddit
We present the first large-scale comparative analysis of Truth Social and the most popular conservative Reddit communities, r/Conservative, r/conservatives, and r/Republican. Using topic modeling with FASTopic and LLM-assisted refinement, we analyze topic prevalence, toxicity, and temporal dynamics across these communities during the first eight months of Truth Social. We find clear contrasts: Truth Social centers on grievance and narrative-driven content, while Reddit focuses more on policy debates. Toxicity is higher on Reddit and peaks in cultural and leader-focused topics. Despite similar event-driven participation shocks across platforms, Truth Social shows higher baseline proportions of users engaging with political topics. Our findings contribute to understanding how alternative right-leaning platforms reshape online discourse.
The First Mass Protest on Threads: Multimodal Mobilization and AI-Generated Visuals in Taiwan’s Bluebird Movement
The 2024 Bluebird Movement in Taiwan marked one of the largest youth-led protests in the country’s democratic history, mobilizing over 100,000 demonstrators in response to parliamentary reforms. Unlike the 2014 Sunflower Movement, Bluebird unfolded within a transformed digital environment dominated by Threads, Meta’s new microblogging platform that draws 24% of its global traffic from Taiwan. Leveraging a dataset of 62,321 posts and 21,572 images, this study analyzes how protest communication developed across textual and visual modalities. We combine LLM zero-shot annotation, gradient-boosting trees, and SHAP explainers, to disambiguate the supply and demand of attention. Results reveal three dynamics: (1) a partisan gap between algorithmic exposure and user engagement, with anti-DPP content surfaced more widely but anti-KMT and pro-DPP content more actively recirculated; (2) textual repertoires centered on commemorations, personal testimonies, and calls to action as key drivers of virality; and (3) a bifurcation in visual strategies, where human photographs concentrated exposure and discussion, while AI-generated animal symbols circulated as mobilization tools and partisan attacks. The exposure-engagement gap demonstrates how Threads functions as both amplifier and filter of democratic contention, suggesting dual publics operating under distinct logics. Generative AI further reshapes symbolic repertoires through kawaii toxicity, or partisan attacks cloaked in cute aesthetics.
Cross-Platform Short-Video Diplomacy: Topic and Sentiment Analysis of China–US Relations on Douyin and TikTok
We examine discussions surrounding China-U.S. relations on the Chinese and American social media platforms Douyin and TikTok. Both platforms, owned by ByteDance, operate under different regulatory and cultural environments, providing a unique perspective for analyzing China-U.S. public discourse. This study analyzed 4,040 videos and 338,209 user comments to assess the public discussions and sentiments on social media regarding China-U.S. relations. Through topic clustering and sentiment analysis, we identified key themes, including economic strength, technological and industrial interdependence, cultural cognition and value pursuits, and responses to global challenges. There are significant emotional differences between China and the US on various themes. Since April 2022, the Chinese government has implemented a new regulation requiring all social media accounts to disclose their provincial-level geolocation information. Utilizing this publicly available data, along with factors such as GDP per capita, minority index, and internet penetration rate, we investigate the changes in sentiment towards the U.S. in mainland China. This study links socioeconomic indicators with online discussions, deeply analyzing how regional and economic factors influence Chinese comments on their views of the US, providing important insights for China-U.S. relationship research and policy making.
Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea
Social media use has been shown to be associated with low fertility desires. However, we know little about the discourses surrounding childbirth and parenthood that people consume online. We analyze 219,127 comments on 668 short videos from Douyin and Tiktok related to reproduction and parenthood from China, South Korea, and Japan, a region famous for its extremely low fertility level, to examine the topics and sentiment expressed in online comments. BERTopic model is used to assist thematic analysis, and a large language model QWen is applied to label sentiment. We find that comments are mainly about child-rearing expenses in all countries, children's utility, particularly in Japan and South Korea, and the rise of individualism, primarily in China. Comments from Douyin exhibit the strongest anti-natalist sentiments, while the Japanese and Korean comments are more balanced. Short video characteristics, such as their views, account type, significantly influence the comment sentiments, alongside regional socioeconomic indicators, including GDP, urbanization, and gender equality level. This work provides one of the first comprehensive analyses of popular discourses on family formation via social media platforms in regions experiencing low fertility rates, making a valuable contribution to our understanding of the spread of family values online.
How Conversational Structure and Style Shape Online Community Experiences
Sense of Community (SOC) is vital to individual and collective well-being. Although social interactions have moved increasingly online, still little is known about the specific relationships between the nature of these interactions and Sense of Virtual Community (SOVC). This study addresses this gap by exploring how conversational structure and linguistic style predict SOVC in online communities, using a large-scale survey of 2,826 Reddit users across 281 varied subreddits. We develop a hierarchical model to predict self-reported SOVC based on automatically quantifiable and highly generalizable features that are agnostic to community topic and that describe both individual users and entire communities. We identify specific interaction patterns (e.g., reciprocal reply chains, use of prosocial language) associated with stronger communities and identify three primary dimensions of SOVC within Reddit – Membership & Belonging, Cooperation & Shared Values, and Connection & Influence. This study provides the first quantitative evidence linking patterns of social interaction to SOVC and highlights actionable strategies for fostering stronger community attachment, using an approach that can generalize readily across community topics, languages, and platforms. These insights offer theoretical implications for the study of online communities and practical suggestions for the design of features to help more individuals experience the positive benefits of online community participation.
(Un)fair Mistakes on Social Media: How Demographic Characteristics Influence Authorship Attribution
Authorship attribution techniques are increasingly being used in online contexts such as sock puppet detection, malicious account linking, and cross-platform account linking. Yet, it is unknown whether these models perform equitably across different demographic groups. Bias in such techniques could lead to false accusations, account banning, and privacy violations disproportionately impacting users from certain demographics. In this paper, we audit authorship attribution for bias in three different ways with respect to gender, native language, and age. First, we evaluate how the proportion of users with a certain demographic characteristic impacts the overall classifier performance. Second, we evaluate if a user's demographic characteristics influence the probability that their texts are misclassified. Our results for these two evaluations indicate that authorship attribution does not demonstrate bias across demographic groups in the closed-world setting. Third, we evaluate the types of errors that occur when the true author is removed from the suspect set, thereby forcing the classifier to choose an incorrect author. This controls for the influence of a users fluctuations in writing style on the mistakes made. Unlike the first two settings, our results here indicate a tendency to attribute authorship to users who share the same demographic characteristic as the true author. Our results highlight that though an NLP model may appear fair in the closed-world setting for a performant classifier, this does not guarantee fairness when errors are inevitable.
Does Geo-Co-Location Matter? A Case Study of Public Health Conversations During COVID-19
Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement. The objective of our research is to analyze the effect of localized engagement on social media conversations. This study examines the impact of geographic co-location, as a proxy for localized engagement. Our research is grounded in a COVID-19 dataset. A key goal during the pandemic for public health experts was to encourage prosocial behavior that could impact local outcomes such as masking and social distancing. Given the importance of local news and guidance during COVID-19, we analyze the effect of localized engagement, between public health experts (PHEs) and the public, on social media. We analyze a Twitter Conversation dataset from January 2020 to November 2021, comprising over 19 K tweets from nearly five hundred PHEs, and 800 K replies from 350 K participants. We use a Poisson regression model to show that geo-co-location is indeed associated with higher engagement. Lexical features associated with emotion and personal experiences were more common in geo-co-located conversations. To complement our statistical analysis, we also applied a large language model (LLM)-based method to automatically generate and evaluate hypotheses; the LLM results confirm the results using lexical features. This research provides insights into how geographic co-location influences social media engagement and can inform strategies to improve public health messaging.
Anti-Establishment Sentiment on TikTok: Implications for Understanding Influence(rs) and Expertise on Social Media
Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media for information, it is imperative to understand whether and how social media environments may be contributing to distrust of institutions. In social media, content creators, influencers, and other opinion leaders often position themselves as having expertise and authority on a range of topics from health to politics, and in many cases devalue and dismiss institutional expertise to build a following and increase their own visibility. However, the extent to which this content appears and whether such content increases engagement is unclear. This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok. Despite its popularity as a source of information, TikTok remains relatively understudied and may provide important insights into how people form attitudes towards institutions. We employ a computational approach to label TikTok posts as containing AES or not across topical domains where content creators tend to frame themselves as experts: finance and wellness. As a comparison, we also consider the topic of conspiracy theories, where AES is expected to be common. We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics. However, we find that engagement patterns with such content vary by area, and that there may be platform incentives for users to post content that expresses anti-establishment sentiment.
Bias in Decision-Making for AI’s Ethical Dilemmas: A Comparative Study of ChatGPT and Claude
Recent advances in Large Language Models (LLMs) have enabled human-like responses across various tasks, raising questions about their ethical decision-making capabilities and potential biases. This study systematically evaluates how nine popular LLMs (both open-source and closed-source) respond to ethical dilemmas involving protected attributes. Across 50,400 trials spanning single and intersectional attribute combinations in four dilemma scenarios (protective vs. harmful), we assess models' ethical preferences, sensitivity, stability, and clustering patterns. Results reveal significant biases in protected attributes in all models, with differing preferences depending on model type and dilemma context. Notably, open-source LLMs show stronger preferences for marginalized groups and greater sensitivity in harmful scenarios, while closed-source models are more selective in protective situations and tend to favor mainstream groups. We also find that ethical behavior varies across dilemma types: LLMs maintain consistent patterns in protective scenarios but respond with more diverse and cognitively demanding decisions in harmful ones. Furthermore, models display more pronounced ethical tendencies under intersectional conditions than in single-attribute settings, suggesting that complex inputs reveal deeper biases. These findings highlight the need for multi-dimensional, context-aware evaluation of LLMs’ ethical behavior and offer a systematic evaluation and approach to understanding and addressing fairness in LLM's decision-making.
Consistency of Responses and Continuations Generated by Large Language Models on Social Media
Large Language Models (LLMs) demonstrate remarkable capabilities in text generation, yet their emotional consistency and semantic coherence in social media contexts remain insufficiently understood. This study investigates how LLMs handle emotional content and maintain semantic relationships through continuation and response tasks using three open-source models: Gemma, Llama3 and Llama3.3 and one commercial Model:Claude. By analyzing climate change discussions from Twitter and Reddit, we examine emotional transitions, intensity patterns, and semantic consistency between human-authored and LLM-generated content. Our findings reveal that while both models maintain high semantic coherence, they exhibit distinct emotional patterns: these models show a strong tendency to moderate negative emotions. When the input text carries negative emotions such as anger, disgust, fear, or sadness, LLM tends to generate content with more neutral emotions, or even convert them into positive emotions such as joy or surprise. At the same time, we compared the LLM-generated content with human-authored content. The four models systematically generated responses with reduced emotional intensity and showed a preference for neutral rational emotions in the response task. In addition, these models all maintained a high semantic similarity with the original text, although their performance in the continuation task and the response task was different. These findings provide deep insights into the emotion and semantic processing capabilities of LLM, which are of great significance for its deployment in social media environments and human-computer interaction design.
Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
The prevalence of short video platforms, combined with the ineffectiveness of age verification mechanisms, raises concerns about the potential harm facing children and teenagers in an algorithm-moderated online environment. We conducted multimodal feature analysis and thematic topic modeling of 4,492 short videos recommended to children and teenagers on Instagram Reels, TikTok, and YouTube Shorts, collected as a part of an algorithm auditing experiment. This feature-level and content-level analysis revealed that unsafe (i.e., problematic, mentally distressing) short videos (a) possess darker visual features and (b) contain explicitly harmful content and implicit harm from anxiety-inducing ordinary content. We introduce a useful framework of online harm (i.e., explicit, implicit, and unintended), providing a unique lens for understanding the dynamic, multifaceted online risks facing children and teenagers. The findings highlight the importance of protecting younger audiences in critical developmental stages from both explicit and implicit risks on social media, calling for nuanced content moderation, age verification, and platform regulation.
Auditing Algorithmic Personalization in TikTok Comment Sections
Personalization algorithms are ubiquitous in modern social computing systems, yet their effects on comment sections remain underexplored. In this work, we conducted an algorithmic auditing experiment to examine comment personalization on TikTok. We trained sock-puppet accounts to exhibit left-leaning or right-leaning preferences and successfully validated 17 of them by analyzing the videos recommended on their For You Pages. We then scraped the comment sections shown to these trained partisan accounts, along with five cold-start accounts, across 65 politically neutral videos related to the 2024 U.S. presidential election that contain abundant discussions from both left-leaning and right-leaning perspectives. We find that while the composition of top comments remains largely consistent for all videos, ranking divergence between accounts from different political groups is significantly greater than that observed within the same group for some videos. This effect is strongly correlated with video-level metrics such as comment volume, engagement inequality, and partisan skew in the comment sections. Furthermore, through an exploratory case study, we find preliminary evidence that personalization can result in comment exposure aligned with an account's political leaning. However, this pattern is not universal, suggesting that the extent of politically oriented comment personalization is context-dependent.
DisImpact: Quantifying the Physi-Social Impact of Natural Disasters Through Social Media
Natural disasters not only cause large-scale physical destruction, but also cascading social consequences that are difficult to quantify with traditional surveys and reports. Social media platforms offer an alternative perspective that captures multimodal, real-time, and user-generated content that can be leveraged for disaster impacts. In this paper, we introduce DisImpact, a two-stage framework that systematically quantifies the physi-social impacts of disasters via a Multimodal Large Language Model (MLLM). The social media posts are first classified into ten disaster impact categories that cover both physical and social domains. We then construct a disaster impact index that integrates the relative prominence of each category with the intensity of public engagement on a weekly basis. This design provides a unified scale for representing disaster impacts across both individual disaster impact categories and the broader physical and social domains. The unified representation enables direct comparison across categories and allows the impacts to be flexibly aggregated to reveal higher-level patterns and overall trends. We validate the impact indices against authoritative ground-truth data, including FEMA Public Assistance data and NASA FIRMS fire detections, observing consistent lead-lag correlations that demonstrate strong validity across both social and physical impact dimensions. We further conduct temporal and spatial analyses, and the results show that physical impacts are often peak during the disasters and localized in regions that are directly affected by disasters, while social impacts often emerge later and spread more broadly across time and space. To the best of our knowledge, this is the first framework to comprehensively quantify disaster impacts across their physical and social dimensions using multimodal data from multiple social media platforms.
PagePilot for Web Automation Based on a Multi-Agent Architecture
Recent advances in reasoning and multi-modal analysis have enabled large language models (LLMs) to automate tasks like extracting data and filling out forms for web navigation. However, existing systems struggle with complex, long-form web interactions, such as parsing lengthy articles or optimizing multi-step workflows, due to limitations in processing dynamic content and scalable architecture. To address this gap, we introduce PagePilot, an automated web control system that integrates DOM source code analysis and multi-agent collaboration for robust task execution. Building on WebVoyager, PagePilot incorporates dynamic loading to handle infinite-scroll pages and asynchronous content, alongside a hierarchical reasoning framework for efficient decision-making. Evaluations demonstrate state-of-the-art performance: a 76% task success rate on WebVoyager (vs. 63% baseline) and 47% on GAIA (vs. 38% for GPT-4). To validate generalization, we curated benchmarks from Mind2Web and a novel Chinese web dataset, achieving 52% and 70% success rates, respectively, outperforming prior methods in linguistically diverse scenarios. Ablation studies confirm critical design choices, showing a 22% increase in task completion and 27% reduction in redundant actions compared to fragmentary approaches.
The Neighborhood: An Interactive Social Media Experience for Increasing Awareness of Automated User Profiling
Internet users leave a range of digital traces that allow for automated profiling, often without their knowledge. Advancements in artificial intelligence (AI) make it easier than ever to automate the inference of user attributes that are not even directly shared, exposing users to privacy risks. One means of protection against this is the widespread promotion of AI literacy to social media users. As an example of how this might be achieved, we present an interactive online simulation with integrated AI tools, designed to raise awareness of AI capabilities for user profiling. Within this experience, users can take actions similar to those that they would everyday: make a post, update their profile, and like existing posts. After the users finish their experience, they may choose to receive a report about an AI model's inferences about their personal attributes along with information about how similar inferences are made about them when using major social media platforms. We find that the level of interaction within the simulation correlated with large language model (LLM) accuracy in profiling. We also observe high accuracy in profiling despite many participants self-identifying as protective of their data. Finally, a majority of participants reported that as a result of taking part in our study, they expect to think more carefully about their online interactions and what they might reveal about themselves in the future. This aligns with our aim to increase AI literacy and empower people to make more informed decisions on what they disclose on the internet.
Disagreement Is Disappearing on U.S. Cable Debate Shows
Prime-time cable news programs are a highly influential part of the American media landscape, with top-rated opinion shows attracting millions of politically attentive viewers each night. In an era of intense political polarization, a critical question is whether these widely-watched "debate" shows foster genuine discussion or have devolved into partisan echo chambers that deepen societal divides. While these programs claim to air competing viewpoints, no large-scale evidence exists to quantify how often hosts and guests actually disagree. Measuring these exchanges is a significant challenge, as live broadcasts contain overlapping speakers, sarcasm, and billions of words of text. To address this gap, we construct the first speaker-resolved map of agreement and disagreement across U.S. cable opinion programming. Our study assembles over 21,000 episodes from 24 flagship shows on Fox News, MSNBC, and CNN from 2010-2024, segmenting them into host-guest turns and labeling 2.13 million turn-pairs using a high-fidelity large-language-model classifier. We present three findings: (1) the proportion of disagreement/debate on prime time shows a consistent downward trend, dropping by roughly one-third between 2017 and 2024; (2) on-air challenge is partisan and asymmetric--conservatives seldom face push-back on Fox, liberals seldom on MSNBC, with CNN declining toward the midpoint; and (3) polarizing issues such as abortion, gun rights, and immigration attract the least disagreement. The work contributes a public corpus, an open-source stance pipeline, and the first longitudinal evidence that televised "debate" is retreating from genuine discussion. By transforming into platforms for partisan affirmation, these shows erode the cross-cutting cleavages essential for a pluralistic society, thereby intensifying affective polarization.
Decentralized Diffusion: Decomposing Information Diffusion in Federated Social Networks
This work examines information diffusion in decentralized social media platforms, specifically on Mastodon due to its federated architecture. Unlike centralized platforms like Twitter, Mastodon distributes content across independently operated yet interconnected servers, known as "instances." In this decentralized environment, inter-instance diffusion plays a critical role in shaping communication patterns. Using a dataset of Mastodon posts, or "toots," related to the Gaza conflict, we analyzed how user-, toot-, and instance-level characteristics drive cross-instance diffusion. Our findings demonstrate that these features collectively shape diffusion, with instance-level characteristics playing a particularly crucial role. This work contributes to the theories of decentralized communication by emphasizing the importance of federated architectures and inter-instance interactions. Practical implications include strategies for platform governance, crisis communication, and misinformation control, with a focus on the potential of decentralized platforms to balance localized interactions with global reach.
Altering Words to Evade Perceived Moderation: Decoding Algospeak in Chinese Social Media Video Captions
Video content creators on social media often modify their caption text to bypass algorithmic moderation as they perceive it -- a practice known as algospeak. This behavior of adjusting expressions online could become especially complicated in Chinese online space where both moderation and censorship overlap, and where certain types of expression are explicitly restricted. In this study, we conduct a large-scale analysis of algospeak by examining over 200,000 movie-recap videos from 96 YouTube channels, most of whom are also prominent on Douyin (the domestic implementation of TikTok in China). We extract the video's hard‑coded captions and employed AI to detect altered terms. We then examine the prevalence and characteristics of these alterations, and leverage two commercial text‑sensitivity detectors to infer the potential motivations and policies driving these algospeak behaviors. We also explore whether factors such as content reach and monetization strategies correlate with creators' propensity to modify captions. We find that the presence of embedded ads in videos as a form of brand deals was negatively associated with overall alteration rates: content creators increased alterations for a few explicitly warned topics while reducing them for other topics. Overall, we argue that algospeak in Chinese videos represents a collectively adopted strategy that both evades perceived moderation and reinforces restrictive norms within online communities, and is partly attributable to a trust gap between users and the platform on the matter of moderation.
DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes
Social media imagery provides a low-latency source of situational information during natural and human-induced disasters, enabling rapid damage assessment and response. While Visual Question Answering (VQA) has shown strong performance in general-purpose domains, its suitability for the complex and safety-critical reasoning required in disaster response remains unclear. We introduce DisasterVQA, a benchmark dataset designed for perception and reasoning in crisis contexts. DisasterVQA consists of 1,395 real-world images and 4,405 expert-curated question–answer pairs spanning diverse events such as floods, wildfires, and earthquakes. Grounded in humanitarian frameworks including FEMA ESF and OCHA MIRA, the dataset includes binary, multiple-choice, and open-ended questions covering situational awareness and operational decision-making tasks. We benchmark seven state-of-the-art vision–language models and find substantial performance variability across question types, disaster categories, regions, and humanitarian tasks. Although models achieve high accuracy on binary questions, they struggle with fine-grained quantitative reasoning, object counting, and context-sensitive interpretation, particularly for underrepresented disaster scenarios. DisasterVQA provides a challenging and practical benchmark to guide the development of more robust and operationally meaningful vision–language models for disaster response.
RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian
We present RoIt-XMASA, a multilingual dataset that extends the Cross-lingual Multi-domain Amazon Sentiment Analysis to Italian and Romanian, comprising 36,000 labeled reviews across three domains (books, movies, and music) and 202,141 unlabeled samples. To address cross-lingual and cross-domain challenges, we propose a multi-target adversarial training framework that employs loss reversal with meta-learned coefficients to dynamically balance sentiment discrimination with domain and language invariance. With our approach, XLM-R achieves an F1-score of 66.23%, outperforming the baseline by 4.64%. Few-shot evaluation shows that Llama-3.1-8B achieves 58.43% F1-score, revealing a meaningful trade-off between the efficiency of prompting-based approaches and the higher performance of task-specific fine-tuning.
Media Cloud 2.0: An Updated Open Web News Archive
We present a completely re-engineered Media Cloud, a massive searchable open source archive of digital news sources and content from around the globe. Since its previous presentation at ICWSM in 2021, the Media Cloud team has re-engineered the tool's data collection, storage, and retrieval systems, built a new front-end research interface, surpassed 1.8 billion stories, and reprocessed all the content to update the extracted metadata with consistent and modern techniques. In this paper we document the new system’s engineering, characterize the datasets to date, and describe user-facing tools. This includes a Directory of online news sources and a searchable Story Index of global news stories. We discuss the utility of the datasets, how they compare to other related work, challenges associated with maintaining open research infrastructure, and research made possible through the datasets and tooling.
Candidata: United States 2024 Elections Candidates and Social Media Handles
We introduce the Candidata dataset, a public archive of social media data for candidates for primary and general federal elections in the United States in 2024. The data collection spans multiple platforms, capturing the diverse ways candidates interact with potential voters, media, and other audiences. By documenting candidate behavior across platforms, this dataset provides an unprecedented resource for analyzing the full scope of social media use in political campaigns. The dataset includes the full list of candidates for federal elections in the United States in 2024, including candidate demographics, election information, and identifiers to link the candidates to other datasets. The social media data includes message content and metadata such as post timestamp and engagement metrics.
The Big Ban Theory: A Pre- and Post-Intervention Dataset of Online Content Moderation Actions
Online platforms rely on moderation interventions to curb harmful behavior such as hate speech, toxicity, and the spread of mis- and disinformation. Yet research on the effects and possible biases of such interventions faces multiple limitations. For example, existing works frequently focus on single or a few interventions, due to the absence of comprehensive datasets. As a result, researchers must typically collect the necessary data for each new study, which limits opportunities for systematic comparisons. To overcome these challenges, we introduce The Big Ban Theory (TBBT) -- a large dataset of moderation interventions. TBBT covers 25 interventions of varying type, severity, and scope, comprising in total over 339K users and nearly 39M posted messages on Reddit and Voat. For each intervention, we provide standardized metadata and pseudonymized user activity collected three months before and after its enforcement, enabling consistent and comparable analyses of intervention effects. In addition, we provide a descriptive exploratory analysis of the dataset, along with several use cases of how it can support research on content moderation. With this dataset, we aim to support researchers studying the effects of moderation interventions and to promote more systematic, reproducible, and comparable research.
WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection
We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years. The dataset was constructed through a rigorous, carefully designed pipeline that integrates keyword-based data collection, semantic deduplication to remove near-duplicate content, and a multi-stage annotation protocol conducted by medical specialists. This process produced a high-quality gold-standard corpus, characterized by substantial inter-annotator agreement and strong reliability for downstream analysis. Additionally, we provide a detailed characterization of WhatsApp misinformation, revealing distinctive linguistic, structural, lexical, temporal, and group-level patterns, as well as a meaningful layer of ambiguous cases that reflect the complexity of health discourse in private messaging. We also benchmark classical models, fine-tuned Small Language Models, and zero- or few-shot Large Language Models under realistic data-scarcity constraints, demonstrating that strong embeddings and LLM approaches perform competitively, while domain alignment and data availability remain critical factors. This study provides a rare, high-quality resource to support misinformation research and computational modeling in encrypted communication environments.
TubeCensus: A Transparent, Replicable, and Large-Scale Census of YouTube Channels and their Subscriber Counts Over Time
YouTube is central to contemporary mass media. However, the official YouTube API does not provide access to the full set of creators or creator metadata on the platform. This lack of basic visibility into the YouTube ecosystem hinders understanding of the platform’s creator economy. Researchers currently have no easy, transparent, or replicable way to construct large scale datasets of YouTube creators and their audiences over time. This makes it challenging to study vital social questions, such as how changes to the YouTube recommendation algorithm shape creator incentives and by extension the mass media on the platform. We address this gap with TubeCensus, a large-scale longitudinal dataset of YouTube creators and subscriber counts, constructed by collecting, linking, and organizing nearly two decades of YouTube page captures from the Internet Archive. This approach is transparent and replicable and does not require interaction with the YouTube API, whose output can change over time. We validate the coverage of TubeCensus against prior estimates of YouTube’s size and find that our resource includes creators responsible for at least 30–36% of all YouTube content. We also find that TubeCensus provides good coverage of prominent creators. To support future research, we hide the substantial complexities of the YouTube identifier system and Internet Archive capture system by distributing our dataset via an easy-to-use pip package. Finally, we use our resource to complete basic exploratory analysis of YouTube channel content and the mechanisms associated with YouTube channel growth.
TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger
Here we present a massive longitudinal dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups, enriched with metadata on forwards, reactions, and polls. The dataset spans multiple languages including Russian and Farsi, representing countries where Telegram shows mainstream adoption, as well as Western languages where Telegram is used in specific sub-communities. The dataset has several advantages. First, when restricted by language, it provides a versatile example of an algorithm-free platform, contrary to many other social media platforms that are strongly influenced by opaque content-curation algorithms. Second, it enables comparative studies across different languages, communities, and user bases under identical platform affordances. The dataset thus offers a foundation for studying engagement patterns, network evolution, and community formation in the absence of algorithmic curation.
Global YouTube Trending Dataset (2022–2025): Three Years of Platform-Curated, Cross-National Trends in Digital Culture
On July 1, 2025, YouTube retired its decade-long public “Trending” pages, ending platform-curated, non-personalized video discovery. The Trending list had long served as a vital lens into algorithmic influence, cultural diffusion, and crisis communication globally, offering a rare platform-curated baseline to study global attention and cultural salience. We present a three-year archival dataset of YouTube Trending videos, collected from July 1, 2022, to June 30, 2025, with four daily snapshots for each of the 104 countries. The dataset includes 446,971 snapshots, each capturing up to 200 trending videos, encompassing 78.4 million video entries (726,627 unique videos) and associated metadata. Each record includes the rank, core identifiers (snapshot time, country) and content metadata (video ID, channel ID, title, description, tags, publication date, category, channel name, language, live status, view counts, and comment counts). Unlike previous datasets with limited geographic scope or short timeframes, our non-personalized data provides exceptional cross-national and longitudinal coverage for studying digital culture, platform governance, and temporal dynamics in content popularity. We document the data collection methodology, schema design, coverage, descriptive statistics for both global and U.S. trending videos, and the ethical safeguards implemented throughout.
MemeMatch: A Large-Scale Dual-Context Multimodal Dataset and Retrieval System for Internet Memes
We introduce MemeMatch, a large-scale multimodal meme dataset and retrieval system that bridges meme collection, annotation, and analysis in a unified pipeline. The dataset contains nearly one million image-with-text memes from Reddit’s r/Memes (2018–2023) and ImgFlip, with rich metadata. Each meme is decomposed into two semantic contexts: local context, capturing the editable text payload (overlay text and title), and global context, capturing the underlying visual substrate or template semantics. Both are enriched with transformer-based annotations, including 14-dimensional sentiment and emotion vectors, BERTopic-derived topics, and zero-shot usage-intent labels. This structured representation supports exploratory analysis and context-aware retrieval by natural language or image query.
Misinformation Span Detection in Videos via Audio Transcripts
Online misinformation is one of the most challenging issues lately, yielding severe consequences, including political polarization, attacks on democracy, and public health risks. Misinformation manifests in any platform with a large user base, including online social networks and messaging apps. It permeates all media and content forms, including images, text, audio, and video. Distinctly, video-based misinformation represents a multifaceted challenge for fact-checkers, given the ease with which individuals can record and upload videos on various video-sharing platforms. Previous research efforts investigated detecting video-based misinformation, focusing on whether a video shares misinformation or not on a video level. While this approach is useful, it only provides a limited and non-easily interpretable view of the problem given that it does not provide an additional context of when misinformation occurs within videos and what content (i.e., claims) are responsible for the video's misinformation nature. In this work, we attempt to bridge this research gap by creating a novel dataset to allow us to explore misinformation detection on videos, focusing on identifying the span of videos that are responsible for the video's misinformation claim (misinformation span detection). We present two new datasets for this task, both containing false claims and the video moment in which they appear. We transcribe each video's audio to text, identifying the video segment in which the misinformation claims appears, resulting in two datasets of more than 500 videos with more than 2,400 segments containing annotated fact-checked claims. Then, we employ classifiers built with state-of-the-art language models, and our results show that we can identify in which part of a video there is misinformation with an F1 score of 0.68. To assist the research community in future research endeavors focusing on misinformation span detection, we make publicly available our annotated datasets that includes false claims and the video spans that these false claims appear in videos. We also release all transcripts, audio and videos.
Crime VIP: A Closed-Access Underground Hacking Forum
We present XIN, the dataset of a closed-access Russian-English underground hacking forum. It contains a collection ≈1.3M posts from over 20 years (Feb 2005-Aug 2025), and - to the best of our knowledge - is the largest collection of a closed-access underground hacking forum available for research. While there is a wide range of underground forum datasets available, there is a lack of non-English forums, and especially closed-access forums. Those are particularly challenging to crawl as the access is gated. Hence, a limited few-shot opportunity requires extreme care when crawling to avoid detection, which leads to account banning. Our stealthy data collection spanned 5 years (2020-2025). The statistical analysis of our data mirrors how cybercrime gradually shifted from technical, hands-on hacking to an industry where different building blocks can be assembled and applied in the absence of a broad or profound technical understanding. This dataset will contribute to a more complete evaluation of the cybercriminal landscape, shedding light on the activity that happens in closed-door, non-English communities.
DualDet: A Dual-Task Detection Benchmark for Stance and Bot Detection on Social Media
Social media manipulation is often driven by automated accounts that amplify polarized viewpoints, posing persistent challenges to online information integrity. Two core capabilities for understanding and mitigating such manipulation are stance detection and bot detection. Although usually studied separately, they are coupled in practice: automation can skew stance distributions, while stance-aligned communities can shape visibility and network signals used for bot detection. We introduce DualDet, a dual-task benchmark for stance and bot detection spanning election discourse (Biden, Trump) and vaccine-related discourse (Vaccine). DualDet integrates inherited bot labels and follower-graph context with expert-annotated user-level stance labels, achieving substantial inter-annotator agreement (Cohen’s k > 0.9). The dataset contains 124,802 users in total, of which 22,906 users are annotated with expert stance labels. We further provide dataset analyses and reproducible baselines for stance detection, bot detection, and joint evaluation, revealing measurable stance–bot dependencies and highlighting open challenges for coupling-aware modeling.
ParsCN: A Persian Dataset for Counter-Narrative Generation to Combat Online Hate Speech
Online hate speech threatens online civility, particularly in low-resource and multilingual environments. Counter-narratives offer a promising solution by promoting constructive responses to hate speech. However, automatic counter-narrative generation is hindered by the lack of high-quality data for low-resource languages like Persian. To bridge this gap, we introduce ParsCN, the first and most comprehensive Persian counter-narrative dataset. Consisting of 1,100 hate speech and counter-narrative pairs, it provides fine-grained annotations across six target groups and six countering strategies, tailored to the socio-cultural context of Persian online discourse. We propose a novel, scalable multi-stage framework that integrates culturally-informed human annotation with few-shot LLM-augmented generation, guided by semantic retrieval and rigorous manual curation. This approach enables the creation of diverse, high-quality counter-narratives while significantly reducing annotation costs—establishing a replicable paradigm for other low-resource settings. Comprehensive human and automatic evaluations confirm the quality of the dataset and the effectiveness of the generated responses. Human-written counter-narratives achieved the highest scores for relevance (4.23), Effectiveness (4.21), fluency (4.92), and tone appropriateness (4.79), with GPT-4o and Claude closely following. Automatic evaluations show strong semantic alignment (BERTScore F1 up to 0.709), high lexical diversity, and low toxicity across all sources. Finally, we conduct benchmark evaluations using mBART and PersianMind on a held-out test set. Results reveal that existing models struggle with fluency, cultural nuance, and safety—highlighting the need for Persian-specific resources like ParsCN. Our dataset serves as a foundational benchmark to advance research on Persian counter-narrative generation and foster safer, more inclusive digital spaces.
DivinWD: Exploring the Diversity of Scientific Publications in Wikidata
Wikidata has emerged as a major open repository of scholarly metadata, yet its characteristics and limitations as a source for diversity analyses are not well documented. We present DivinWD (DIVersity IN WikiData), a curated dataset consisting of more than 23 million triples, produced via a fully open-source processing pipeline to enable the study of diversity in scientific publications represented in Wikidata. The dataset comprises over 1.2 million scholarly articles published between 2010 and 2024, enriched by integrating Wikidata with 5 external bibliographic sources--Crossref, Dimensions, OpenAlex, Scopus, and Semantic Scholar--and augmenting them using the Genderize API, to enrich metadata on language, field of study, authorship, gender, geographic origin, and institutional affiliation. Our analysis documents systematic coverage biases and infrastructural artifacts affecting Wikidata's scholarly content, highlighting important considerations for reuse. By releasing the dataset and pipeline, this work provides a transparent foundation for future research on diversity in science and for the development and evaluation of open, reproducible bibliometric indicators.
Ghostwriters of the Marketplace: A Corpus of Machine-Generated Reviews for Google Services and Businesses
Generative models are simplifying the process of creating fake reviews. Models such as ChatGPT or DeepSeek can be used with ease to generate fake reviews with various ratings. However, these fake reviews can mislead buyers/readers. We generated a dataset with 78k reviews (building our "main" dataset) generated by 6 LLMs using three different writing styles, 117 tones and three ratings (positive, negative and neutral). We used 50 prompts, multi-turn prompting and paraphrasing to generate a diverse range of reviews. Additionally, we created another, smaller dataset (external), using the same prompts and tones as before, but using three additional LLMs. This dataset allows detectors of AI-generated text to be tested on their ability to detect texts created using similar prompts but before unseen LLMs. We performed first tests using the newly generated dataset for AI-generated reviews detection. We fine-tuned RoBERTa on our dataset and, for testing generalizability, on the benchmark RAID using Reddit posts, news, and IMDb reviews. On the test splits of seen data, the detector achieved satisfactory results with an F1-score of 99.98%. On RAID the performance dropped, showing potential future research directions.
SemioMeme: A Symbolic–Subsymbolic Knowledge Graph Dataset for Multimodal Meme Analysis
Internet memes present a challenge for computational analysis as their meaning derives from cultural context external to their observable features; however, visually similar memes carry distinct cultural meanings, whilst semantically related memes may share no perceptual similarity resulting in a decoupling of format and meaning. To support analysis requiring both, we present SemioMeme, a knowledge graph providing symbolic representations of meme concepts with their cultural connections alongside subsymbolic vision and text embeddings connected via a dedicated property between both. This supports hybrid queries that can surface cultural associations accruing through graph proximity, often invisible to similarity search or explicit labelling alone. The resource, including source data and code, is made openly available and covers 16,707 meme concepts, 507K meme instances with multimodal embeddings, and 7.2M RDF triples.
GoogleTrendArchive: A Year-Long Archive of Real-Time Web Search Trends Worldwide
GoogleTrendArchive is a comprehensive archive of Google Trending Now data spanning over one year (from November 28, 2024 to January 3, 2026) across 125 countries and 1,358 locations. Unlike Google Trends, which requires specifying search terms in advance, Trending Now captures search queries experiencing real-time surges, offering a way to inductively discover trending patterns across regions for studying collective attention dynamics. However, Google does not provide historical access to this data beyond seven days. Our dataset addresses this gap by presenting an archive of Trending Now data. The dataset contains over 7.6 million trend episodes. Each record includes the trend identifier, search volume bucket, precise timestamps, duration, geographic location, and related query clusters. This dataset, among other, enables systematic studies of information diffusion patterns, cross-cultural attention dynamics, crisis responses, and the temporal evolution of collective information-seeking at a global scale. The comprehensive geographic coverage facilitates fine-grained cross-country or cross-regional comparative analyses.
The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias
We present MediaSpin, a large-scale language resource capturing how major news outlets modify headlines after publication, and MediaSpin-in-the-Wild, a complementary dataset linking these revised headlines to their downstream engagement on social media. The increasing editability of online news headlines offers new opportunities to study linguistic framing and bias through the lens of editorial revisions. The dataset contains 78,910 headline pairs annotated for 13 types of media bias, grounded in established media-bias taxonomies, covering both subjective (e.g., sensationalism, spin) and objective (e.g., omission, slant) forms, with annotation conducted through a human-supervised large-language-model pipeline with expert validation and quality control. We describe the annotation schema and demonstrate three downstream applications: (1) cross-national analysis of how country references are added or removed during editing, (2) transformer-based bias classification at both binary and fine-grained levels, and (3) behavioral analysis of biased headlines on X (Twitter) using 180,786 news-related tweets from 819 consenting users. The results reveal regional asymmetries in representational framing, measurable linguistic markers, and consistently higher engagement with biased content. MediaSpin and MediaSpin-in-the-Wild together provide a reproducible benchmark for bias detection and the study of editorial and behavioral dynamics in contemporary media ecosystems.
MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane
Natural disasters cause multidimensional threats to human societies, with hurricanes exemplifying one of the most disruptive events that not only caused severe physical damage but also sparked widespread discussion on social media platforms. Existing datasets for studying societal impacts of hurricanes often focus on outdated hurricanes and are limited to a single social media platform, failing to capture the broader societal impact in today's diverse social media environment. Moreover, existing datasets annotate visual and textual content of the post separately, failing to account for the multimodal nature of social media posts. To address these gaps, we present a multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane (MASH) that includes 59,607 relevant social media data posts from Reddit, TikTok, and YouTube. In addition, all relevant social media data posts are annotated in a multimodal approach that considers both textual and visual content on three dimensions: Humanitarian Classes, Bias Classes, and Information Integrity Classes. To our best knowledge, MASH is the first large-scale, multi-platform, multimodal, and multi-dimensionally annotated dataset centered on hurricane disasters. In addition, we introduce an online platform that supports interactive data exploration, provides preliminary analytical results, and allows users to share their insights regarding the societal impacts of hurricanes. We envision that MASH can contribute to the study of hurricanes' impact on society, such as disaster severity classification, public sentiment analysis, disaster policy making, and bias identification.
Detecting Divisive Language: A Concept-Grounded, LLM-Guided Pipeline for Polarizing Social Media Sphere
Political polarization poses a growing global challenge, yet existing NLP approaches typically rely on indirect proxies such as toxicity or sentiment, which fail to capture identity-based antagonism that is central to polarizing discourse. We address this gap by conceptualizing polarization-related discourse as divisive language: language that explains political or social disagreement by attributing it to group-based identities. Building on this definition, we propose a staged training pipeline that uses large language models (LLMs) to generate definition-grounded supervision and progressively distills it into lightweight classifiers suitable for large-scale analysis. Experiments on social media data show that the resulting models substantially outperform zero-shot prompting and small-scale supervised baselines, while detecting forms ofpolarization that are not captured by toxicity- or sentiment-based methods. Our findings demonstrate that divisive language can be treated as a distinct, computable linguistic construct, enabling scalable and theoretically grounded analysis of political polarization.
Seeking Help, Facing Harm: Auditing TikTok's Mental Health Recommendations
Recommender systems on social media increasingly mediate how users encounter mental health content, yet it remains unclear whether they distinguish help-seeking from distress expression. We conduct a controlled 7-day audit of TikTok's "For You" page using 30 fresh accounts and LLM-guided agents that vary initial search framing (distress- vs. help-initiated) and interaction strategy (engaged, avoidant, passive). Across 8,727 recommended videos, interaction behavior dominates exposure outcomes: engagement rapidly saturates feeds with mental health content (≃45% of daily recommendations), while avoidance and passive viewing reduce but do not eliminate exposure (≃11–20%). Search framing mainly shifts composition rather than volume—help-initiated searches yield more potentially supportive material, yet potentially harmful content persists at low but non-zero levels, including content in the Suicide/Self-Harm category. These findings suggest limited sensitivity to user intent signals in TikTok's recommendations and motivate context-aware safeguards for sensitive topics.
Do Students Still Ask Each Other? Evidence from Reddit on Test Preparation in the Age of Generative AI
The growing availability of generative AI tools has drawn attention to their potential influence on how students prepare for standardized tests. We examine whether ChatGPT’s launch coincided with changes in SAT-related help-seeking behavior on Reddit using submission posts from SAT-focused subreddits and interrupted time-series analyses. Focusing on requests for peer explanations and strategy-related guidance, we find no evidence of an immediate decline in peer explanation requests overall or by subject, with subject-specific patterns largely following preexisting trends. In contrast, strategy-related help-seeking exhibits a significant increase in post-launch growth, suggesting a shift toward higher-level planning support. Overall, these findings indicate that generative AI did not displace peer-based SAT support but may selectively reshape how students seek help in online communities.
How Do Parents Use Online Communities? An Analysis of the r/Parenting Subreddit
As today’s parents navigate the challenges of raising a child, many are turning to online spaces where fellow parents share their experiences. To better understand parents' use of online communities, we conduct a two-part analysis of data from (1) the r/Parenting subreddit and (2) a nationally representative survey of U.S. parents. Based on 29,295 posts and 853,209 comments collected from r/Parenting, we find that about half of posts express negative emotions — yet the vast majority of comments in reply to these posts are supportive. We also surface themes from three salient topics that appear in posts on the subreddit. Survey data from a sample of 3,054 parents of children ages 12 and under complements the Reddit analysis by providing insight on how parents use online communities in day-to-day life. We find that about two-thirds of U.S. parents say they visit online parenting communities, and that mothers, young parents, parents with young children, and parents with lower incomes are especially likely to do so. Overall, we leverage both data sources to characterize who participates in online parenting communities, what they discuss there, and how parents experience these online spaces.
Beyond AI: Exploring Retained Improvement of Reasoning Ability in LLM-Assisted Human Credibility Assessment on Social Media
As Large Language Models (LLM) become increasingly deployed to social media content evaluation, understanding their impact on human independent judgment is critical. This study investigates whether LLM-based assistance can lead to retained improvements in credibility assessment of climate-related social media posts. We designed a two-step assistant combining sidebar advisors with optional chatbot interaction and conducted a three-phase user study (N = 31), including a baseline phase without LLM, an LLM-supported phase, and an evaluation phase without LLMs. Results show that users became significantly more skeptical when supported by LLM and retained cautious evaluative stance even after support was withdrawn. Moreover, the content analysis indicates that LLM assistance led to a retained improvement in participants’ reasoning ability. Participants reported moderate cognitive load and low frustration, while expressing strong gains in confidence, reflection, and critical thinking. These findings suggest that LLM assistants can produce a partially retained improvement of reasoning ability and shift toward more cautious credibility assessments.
Tracking the Temporal Dynamics of News Coverage of Catastrophic and Violent Events
The modern news cycle has been fundamentally reshaped by the rapid exchange of information online. As a result, media framing shifts dynamically as new information, political responses, and social reactions emerge. Understanding how these narratives form, propagate, and evolve is essential for interpreting public discourse during moments of crisis. In this study, we examine the temporal and semantic dynamics of reporting for violent and catastrophic events using a large-scale corpus of 126,602 news articles collected from online publishers. We quantify narrative change through publication volume, semantic drift, semantic dispersion, and term relevance. Our results show that sudden events of impact exhibit structured and predictable news-cycle patterns characterized by rapid surges in coverage, early semantic drift, and gradual declines toward the baseline. In addition, our results indicate the terms that are driving the temporal patterns.
You See It, They Don't: An Exploratory Study of User-to-User Variation in Instagram Comments
In March 2025, Meta announced a new AI system to rank the order of the comments shown to users. With existing research showing how feed personalization systems can lead to increased polarization, the introduction of this new system raises similar questions. This paper presents a small-scale exploratory study examining whether the ranking system produces systematic differences in visible comments shown to different users, particularly for news-related content. Using four sock-puppet accounts varying in gender and political leaning, we collect visible comments on posts from ten news and ten non-news accounts. This collection is repeated twice from two VPN locations to assess location effects. We ask 1) how many visible comments vary across different users, 2) is this variation higher for news accounts than non-news accounts, and 3) can user-attributes like gender, political leaning, and location systematically explain the observed variation. Contrary to our expectations, we find that visible comments on news posts are less likely to vary across users than those on non-news posts. Variation is better explained by account metrics like comment and follower counts than by user attributes. These findings provide an initial glimpse into personalized comment ranking on Instagram and motivate larger, more systematic audits of how comment personalization may shape online discourse. To support further research, we provide the code to collect comments and the data upon request.
Reality Check: Measuring Real-World Applicability of State-of-the-Art Audio Deepfake Detectors on Social Media Data
Audio deepfakes are becoming both more realistic and easier to create. At the same time, several audio deepfake detectors have been developed. While some of these have been evaluated using real-world data, there has been no in-depth analysis of their performance in real-world settings. We evaluate five SOTA detectors on two real-world social media datasets. Currently, the equal error rate (EER) is mostly used to evaluate audio deepfake detectors. However, when using the EER, the threshold for classifying whether a recording is genuine or not is calculated based on the prediction scores of the test set. In real-world scenarios, this threshold must be set in advance. We are the first to test the performance of SOTA detectors using varying, beforehand set, thresholds, thereby creating a real-world setting. We found degradations on the ITW test set (e.g. F1: 91.35%- 64.65%) when using other thresholds as set with EER calculation. The SocialDF dataset was found to be especially challenging, with an F1-score of 52.92% achieved using an EER threshold. Using pre-set thresholds resulted in an even lower performance of 50.89%, demonstrating that current detectors are unable to reliably detect real-world audio deepfakes.
Improving Reproducibility in Computational Social Science with the Methods Hub
Computational methods have become central to contemporary social science research, ranging from classical statistical modeling and simulation to large-scale computational social science enabled by organic data. While these developments rely on increasingly complex and compute-intensive analysis pipelines, reproducibility remains a major challenge. The Methods Hub is a community-driven service developed at GESIS to address this gap by enabling reproducible, executable, and well-documented computational methods and tutorials tailored to the needs of social scientists. It combines curated content, persistent identifiers, and seamless integration with interactive execution environments, lowering technical barriers while promoting best practices in open and reproducible science. In our ICWSM 2026 demonstration, we will show how to submit computational methods to the Methods Hub (methodshub.gesis.org) and how the editorial workflow helps make contributors’ work more reusable, executable, and accessible to a wider audience.
We present an interactive visualization for exploring a large-scale Telegram dataset. The demo primarily features (1) the interactive exploration of information flows between channels/groups and (2) a content analysis based on established NLP techniques. It demonstrates how diverse information extracted from complex, heterogeneous social media data can be integrated into a cohesive interface. The demo highlights the potential of technical tools to support interdisciplinary research, such as studies on disinformation, and provides users with interpretable insights for further analysis.
Wikipedia relies on the contributions of hundreds of thousands of volunteers across hundreds of language communities. The long-term health of Wikipedia's editor base is essential for the platform's stability and growth, and assessing it requires metrics that are reproducible, comparable over time, and regularly updated. Prior work introduced the Community Vital Signs, a set of six language-independent indicators capturing editor retention, stability, generational balance, role renewal, and global participation. In this paper, we present a live demonstration of the Wikipedia Community Health Dashboards, a set of interactive visualizations for exploring these indicators across all Wikipedia language editions. The dashboards are powered by a fully automated, open-source pipeline that updates the metrics monthly. We describe the system architecture and showcase how the dashboards enable researchers and community members to monitor and compare community health over time.
APD: Ad Persuasion Dashboard — Insights into Facebook Political Campaign Strategies
This demo presents an interactive dashboard for analyzing persuasive content in political social media advertising. We leverage the APA22 dataset, comprising 56,958 unique Facebook ads from the 2022 Australian Federal Election. The system is powered by PPAsy-XLNet, a lightweight transformer model that achieves state-of-the-art persuasion detection while requiring 86.8% less training data and up to 77.6% fewer parameters than leading ensembles. Our tool allows researchers to visualize campaign dynamics, revealing that highly persuasive ads receive 48.2% more financial investment, peak in volume by 4.8 times before election day, and utilize distinct action-oriented linguistic patterns. This system provides a scalable framework for auditing digital political campaigns.