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
Conversation Kernels: A Flexible Mechanism to Learn Relevant Context for Online Conversation Understanding
Understanding online conversations has attracted research attention with the growth of social networks and online discussion forums. Content analysis of posts and replies in online conversations is difficult because each individual utterance is usually short and may implicitly refer to other posts within the same conversation. Thus, understanding individual posts requires capturing the conversational context and dependencies between different parts of a conversation tree and then encoding the context dependencies between posts and comments/replies into the language model. To this end, we propose a general-purpose mechanism to discover appropriate conversational context for various aspects about an online post in a conversation, such as whether it is informative, insightful, interesting or funny. Specifically, we design two families of Conversation Kernels, which explore different parts of the neighborhood of a post in the tree representing the conversation and through this, build relevant conversational context that is appropriate for each task being considered. We apply our developed method to conversations crawled from slashdot.org, which allows users to apply highly different labels to posts, such as `insightful', `funny', etc., and therefore provides an ideal experimental platform to study whether a framework such as Conversation Kernels is general-purpose and flexible enough to be adapted to disparately different conversation understanding tasks. We perform extensive experiments and find that context-augmented conversation kernels can significantly outperform transformer-based baselines, with absolute improvements in accuracy up to 20% and up to 19% for macro-F1 score. Our evaluations also show that conversation kernels outperform state-of-the-art large language models including GPT-4. We also showcase the generalizability and demonstrate that conversation kernels can be a general-purpose approach that flexibly handles distinctly different conversation understanding tasks in a unified manner.
Copyright (c) 2025 Association for the Advancement of Artificial Intelligence
Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models
This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users and extract longitudinal aggregates of emotions and attitudes with established questionnaires. We focus our analysis on the beginning of the COVID-19 pandemic that had a strong impact on public opinion and collective emotions. We validate our estimates against representative British survey data and find strong positive and significant correlations for several collective emotions. The estimates obtained are robust across multiple training seeds and prompt formulations, and in line with collective emotions extracted using a traditional classification model trained on labeled data. We demonstrate the flexibility of our method on questions of public opinion for which no pre-trained classifier is available. Our work extends the analysis of affect in LLMs to a longitudinal setting through Temporal Adapters. It enables flexible and new approaches to the longitudinal analysis of social media data.
Timeliness Matters: Leveraging Reinforcement Learning on Social Media Data to Prioritize High-Risk Conversations for Promoting Youth Online Safety
Ensuring the online safety of youth has motivated research towards the development of machine learning (ML) methods capable of accurately detecting social media risks after-the-fact. However, for these detection models to be effective, they must proactively identify high-risk scenarios (e.g., sexual solicitations, cyberbullying) to mitigate harm. This `real-time' responsiveness is a recognized challenge within the risk detection literature. Therefore, this paper presents a novel two-level framework that first uses reinforcement learning to identify conversation stop points to prioritize messages for evaluation. Then, we optimize state-of-the-art deep learning models to accurately categorize risk priority (low, high). We apply this framework to a time-based simulation using a rich dataset of 23K private conversations with over 7 million messages donated by 194 youth (ages 13-21). We conducted an experiment comparing our new approach to a traditional conversation-level baseline. We found that the timeliness of conversations significantly improved from over 2 hours to approximately 16 minutes with only a slight reduction in accuracy (0.88 to 0.84). This study advances real-time detection approaches for social media data and provides a benchmark for future training reinforcement learning that prioritizes the timeliness of classifying high-risk conversations.
Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech
Examining the factors that the counterspeech uses is at the core of understanding the optimal methods for confronting hate speech online. Various studies have assessed the emotional base factors used in counter speech, such as emotional empathy, offensiveness, and hostility. To better understand the counterspeech used in conversations, this study distills persuasion modes into reason, emotion, and credibility and evaluates their use in two types of conversation interactions: closed (multi-turn) and open (single-turn) concerning racism, sexism, and religious bigotry. The evaluation covers the distinct behaviors seen with human-sourced as opposed to machine-generated counterspeech. It also assesses the interplay between the stance taken and the mode of persuasion seen in the counterspeech. Notably, we observe nuanced differences in the counterspeech persuasion modes used in open and closed interactions—especially in terms of the topic, with a general tendency to use reason as a persuasion mode to express the counterpoint to hate comments. The machine-generated counterspeech tends to exhibit an emotional persuasion mode, while human counters lean toward reason. Furthermore, our study shows that reason tends to obtain more supportive replies than other persuasion modes. The findings highlight the potential for incorporating persuasion modes into studies about countering hate speech, as they can serve as an optimal means of explainability and pave the way for the further adoption of the reply’s stance and the role it plays in assessing what comprises the optimal counterspeech.
News Source Credibility Assessment: A Reddit Case Study
We present a transformer-based model for credibility assessment, CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), fine-tuned for Reddit submissions focusing on political discourse. We adopt a semi-supervised training approach for CREDiBERT, leveraging the community structure of Reddit. By encoding submission content using CREDiBERT and integrating it with a classification neural network, we improve the credibility assessment for Reddit submission by 3% in F1 score compared to existing methods. Additionally, we introduce a new version of the post-to-post network in Reddit that efficiently encodes user interactions to enhance the credibility assessment task by 8% in the F1 score. We demonstrate CREDiBERT's applicability by evaluating the susceptibility of Reddit communities to different topics and assessing the credibility score of unseen sources.
Structural Group Unfairness: Measurement and Mitigation by Means of the Effective Resistance
Social networks contribute to the distribution of social capital, defined as the relationships, norms of trust and reciprocity within a community or society that facilitate cooperation and collective action. Therefore, better positioned members in a social network benefit from faster access to diverse information and higher influence on information dissemination. A variety of methods have been proposed in the literature to measure social capital at an individual level. However, there is a lack of methods to quantify social capital at a group level, which is particularly important when the groups are defined on the grounds of protected attributes. To fill this gap, we propose to measure the social capital of a group of nodes by means of the effective resistance and emphasize the importance of considering the entire network topology. Grounded in spectral graph theory, we introduce three effective resistance-based measures of group social capital, namely group isolation, group diameter and group control, where the groups are defined according to the value of a protected attribute. We denote the social capital disparity among different groups in a network as structural group unfairness, and propose to mitigate it by means of a budgeted edge augmentation heuristic that systematically increases the social capital of the most disadvantaged group. In experiments on real-world networks, we uncover significant levels of structural group unfairness when using gender as the protected attribute, with females being the most disadvantaged group in comparison to males. We also illustrate how our proposed edge augmentation approach is able to not only effectively mitigate the structural group unfairness but also increase the social capital of all groups in the network.
Deceptive Sound Therapy on Online Platforms: Do Mental Wellbeing Tracks Conform to User Expectations?
The rising popularity of mental wellbeing technologies has led many individuals to explore binaural beats—an emerging form of sound therapy proliferating on web and mobile platforms. However, it currently remains unknown whether users can trust binaural tracks on online platforms, or if they deceive unsuspecting users. Our research aims to address this problem by understanding (1) what binaural beats listeners expect from tracks and (2) whether online tracks conform to these expectations. To understand user expectations, we perform thematic analysis on online forum threads and blog posts to extract binaural beats goals and expectations tied to these goals. Next, we design a methodology to measure binaural beats tracks’ conformance to commonly held user expectations. This methodology comprises, (1) obtaining a track’s intent to induce a mental state through track metadata analysis, (2) extracting a track’s binaural beats time-frequency model using Fast Fourier Transform, (3) mapping user expectations to rules that identify deceptive tracks, and validating them on the track’s extracted intent and time-frequency model. We evaluate ∼7K binaural beats tracks and find that only 7.5% conform to commonly held user expectations, while the remaining 92.5% deceive users with deviant claims (e.g., eroticism, weight loss) or deliver contradicting binaural beats. Our study underscores the significance of understanding users’ expectations and verifying conformance of online wellness technologies to expose discrepancies in expectations.
What's in a Prompt?: A Large-Scale Experiment to Assess the Impact of Prompt Design on the Compliance and Accuracy of LLM-Generated Text Annotations
Manually annotating data for computational social science tasks can be costly, time-consuming, and emotionally draining. While recent work suggests that LLMs can perform such annotation tasks in zero-shot settings, little is known about how prompt design impacts LLMs' compliance and accuracy. We conduct a large-scale multi-prompt experiment to test how model selection (GPT-4o, GPT-3.5, PaLM2, and Falcon7b) and prompt design features (definition inclusion, output type, explanation, and prompt length) impact the compliance and accuracy of LLM-generated annotations on four highly relevant and diverse CSS tasks (toxicity, sentiment, rumor stance, and news frames). Our results show that LLM compliance and accuracy are prompt-dependent. For instance, prompting for numerical scores instead of labels reduces all LLMs' compliance and accuracy. Concise prompts can significantly reduce prompting costs but also lead to lower accuracy on tasks like toxicity. Furthermore, minor prompt changes like asking for an explanation can cause large changes in the distribution of LLM-generated labels. By assessing the impact of prompt design on the quality and distribution of LLM-generated annotations, this work serves as both a practical guide and a warning for using LLMs in CSS research.
Link Me Baby One More Time: Social Music Discovery on Spotify
We explore the social and contextual factors that influence the outcome of person-to-person music recommendations and discovery. Specifically, we use data from Spotify to investigate how a link sent from one user to another results in the receiver engaging with the music of the shared artist. We consider several factors that may influence this process, such as the strength of the sender-receiver relationship, the user's role in the Spotify social network, their music social cohesion, and how similar the new artist is to the receiver's taste. We find that the receiver of a link is more likely to engage with a new artist when (1) they have similar music taste to the sender and the shared track is a good fit for their taste, (2) they have a stronger and more intimate tie with the sender, and (3) the shared artist is popular amongst the receiver's connections. Finally, we use these findings to build a Random Forest classifier to predict whether a shared music track will result in the receiver's engagement with the shared artist. This model elucidates which type of social and contextual features are most predictive, although peak performance is achieved when a diverse set of features are included. These findings provide new insights into the multifaceted mechanisms underpinning the interplay between music discovery and social processes.
Effects of Research Paper Promotion via ArXiv and X
In the evolving landscape of scientific publishing, it is important to understand the drivers of high-impact research, to equip scientists with actionable strategies to enhance the reach of their work, and to understand trends in the use of modern scientific publishing tools to inform their further development. Here, based on a dataset of over 0.5 million publications in computer science and physics, we study trends in the use of early preprint publications and revisions on ArXiv and the use of X (formerly Twitter) for promotion of such papers. We find that early submissions to ArXiv and promotion on X have soared in recent years. Estimating the effect that the use of each of these modern affordances has on the number of citations of scientific publications, we find that peer-reviewed conference papers in computer science that are submitted early to ArXiv gain on average 21.1 ± 17.4 more citations, revised on ArXiv gain 18.4 ± 17.6 more citations, and promoted on X gain 44.4 ± 8 more citations in the first 5 years from an initial publication. In contrast, journal articles in physics experience comparatively lower boosts in citation counts, with increases of 3.9 ± 1.1, 4.3 ± 0.9, and 6.9 ± 3.5 citations respectively for the same interventions. Our results show that promoting one's work on ArXiv or X has a large impact on the number of citations, as well as the number of influential citations computed by Semantic Scholar, and thereby on the career of researchers. These effects are present also for publications in physics, but they are relatively smaller. The larger relative effect sizes, effects of promotion accumulating over time, and elevated unpredictability of the number of citations in computer science than in physics suggest a greater role of world-of-mouth spreading in computer science than in physics. We discuss the far-reaching implications of these findings for future scientific publishing systems and measures of scientific impact.
Bootstrapping Social Networks: Lessons from Bluesky Starter Packs
Microblogging is a crucial mode of online communication. However, launching a new microblogging platform remains challenging, largely due to network effects. This has resulted in entrenched (and undesirable) dominance by established players, such as X/Twitter. To overcome these network effects, Bluesky, an emerging microblogging platform, introduced starter packs — curated lists of accounts that users can follow with a single click. We ask if starter packs have the potential to tackle the critical problem of social bootstrapping in new online social networks. We assess whether starter packs have indeed been helpful in supporting Bluesky growth. Our dataset includes 25.05 × 10⁶ users and 335.42 × 10³ starter packs with 1.73 × 10⁶ members, covering the entire lifecycle of Bluesky. We study the usage of these starter packs, their ability to drive network and activity growth, and their potential downsides. We also quantify the benefits of starter packs for members and creators on user visibility and activity while identifying potential challenges. By evaluating starter packs’ effectiveness and limitations, we contribute to the broader discourse on platform growth strategies and competitive innovation in the social media landscape.
How (Un)ethical Are Instruction-Centric Responses of LLMs? Unveiling the Vulnerabilities of Safety Guardrails to Harmful Queries
In this study, we tackle a growing concern around the safety and ethical use of large language models (LLMs). Despite their potential, these models can be tricked into producing harmful or unethical content through various sophisticated methods, including `jailbreaking' techniques and targeted manipulation. Our work zeroes in on a specific issue: to what extent LLMs can be led astray by asking them to generate responses that are instruction-centric such as a pseudocode, a program or a software snippet as opposed to vanilla text. To investigate this question, we introduce TechHazardQA, a dataset containing complex queries which should be answered in both text and instruction-centric formats (e.g., pseudocodes), aimed at identifying triggers for unethical responses. We query a series of LLMs -- Llama-2-13b, Llama-2-7b, Mistral-V2 and Mistral 8X7B -- and ask them to generate both text and instruction-centric responses. For evaluation we report the harmfulness score metric as well as judgements from GPT-4 and humans. Overall, we observe that asking LLMs to produce instruction-centric responses enhances the unethical response generation by 2-38% across the models. As an additional objective, we investigate the impact of model editing using the ROME technique, which further increases the propensity for generating undesirable content. We observe that the propensity to generate unethical content through instruction-centric responses in comparison to text responses increases significantly with a single edit, rising from an average of 18.9% to 56.7% in zero-shot scenarios, from 31.9% to 56.6% in zero-shot CoT, and from 22.8% to 65.7% in few-shot scenarios.
Retrieval Augmented Encoder-Decoder with Diffusion for Sequential Hashtag Recommendation in Disaster Events
During disasters, access to timely and accurate information is crucial for effective response and recovery efforts. Hashtags have emerged as a lifeline in disaster response, organizing and disseminating critical information on social media, facilitating effective communication and real-time situational awareness. However, existing methods for hashtag recommendation fall short during disasters. Retrieval-based methods rely on fixed predefined hashtag lists, failing to capture dynamic information flow, while generation-based methods lack guidance for generating relevant hashtags. In view of the above, we propose a novel three-stage framework. First, the retriever identifies potential candidate hashtags from a vast collection of tweets annotated with hashtags. Next, a selector narrows down these candidates by analyzing the input tweet and ensuring only the most relevant hashtags are retained. Finally, a diffusion-based seq2seq encoder-decoder generates informative hashtags by leveraging the refined set of candidate hashtags and the original input tweet. The framework infused with diffusion overcomes the limitations of extant encoder-decoder models that produce generic hashtags due to reliance on maximizing training data likelihood. Our diffusion-based approach excels at capturing the dynamic and informal language of disaster situations by reversing a gradual noising process, allowing it to explore wider possibilities and generate more diverse hashtags. We enhance the generator with self-conditioning for better utilization of predicted sequence information. Furthermore, we devise an adaptive nonlinear noise schedule for balanced denoising across time steps for each token in the generated hashtag sequence. Empirical evaluations reveal that our proposed method exhibits superior performance compared to state-of-the-art hashtag recommendation methods in both the quality of generated hashtags and training time.
Susceptibility of Communities Against Low-Credibility Content in Social News Websites
Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites is the formation of low-credibility communities, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities prone to uncredible or highly biased news within a social news website. We employ a user embedding pipeline that detects user communities based on their stances toward posts and news sources. We then project each community onto a credibility-bias space and analyze the distributional characteristics of each projected community to identify those that have a high risk of adopting beliefs with low credibility or high bias. This approach also enables the prediction of individual users' susceptibility to low-credibility content based on their community affiliation. Our results show that latent space clusters effectively indicate the credibility and bias levels of their users, with significant variance observed across clusters---a 34% difference in the users' susceptibility to low-credibility content and a 8.3% difference in the users' susceptibility to high political bias.
Political Elites in the Attention Economy: Visibility Over Civility and Credibility?
Elected officials have privileged roles in public communication. In contrast to national politicians, whose posting content is more likely to be closely scrutinized by a robust ecosystem of nationally focused media outlets, sub-national politicians are more likely to openly disseminate harmful content with limited media scrutiny. In this paper, we analyze the factors that explain the online visibility of over 6.5K unique state legislators in the US and how their visibility might be impacted by posting low-credibility or uncivil content. We conducted a study of posting on Twitter and Facebook (FB) during 2020-21 to analyze how legislators engage with users on these platforms. The results indicate that distributing content with low-credibility information attracts greater attention from users on FB and Twitter for Republicans. Conversely, posting content that is considered uncivil on Twitter receives less attention. A noticeable scarcity of posts containing uncivil content was observed on FB, which may be attributed to the different communication patterns of legislators on these platforms. In most cases, the effect is more pronounced among the most ideologically extreme legislators. Our research explores the influence exerted by state legislators on online political conversations, with Twitter and FB serving as case studies. Furthermore, it sheds light on the differences in the conduct of political actors on these platforms. This study contributes to a better understanding of the role that political figures play in shaping online political discourse.
Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity
Meme clustering is critical for toxicity detection, virality modeling, and typing, but it has received little attention in previous research. Clustering similar Internet memes is challenging due to their multimodality, cultural context, and adaptability. Existing approaches rely on databases, overlook semantics, and struggle to handle diverse dimensions of similarity. This paper introduces a novel method that uses template-based matching with multi-dimensional similarity features, thus eliminating the need for predefined databases and supporting adaptive matching. Memes are clustered using local and global features across similarity categories such as form, visual content, text, and identity. Our combined approach outperforms existing clustering methods, producing more consistent and coherent clusters, while similarity-based feature sets enable adaptability and align with human intuition. We make all supporting code publicly available to support subsequent research.
“I’m Not for Sale” – Perceptions and Limited Awareness of Privacy Risks by Digital Natives About Location Data
Although mobile devices benefit users in their daily lives in numerous ways, they also raise several privacy concerns. For instance, they can reveal sensitive information that can be inferred from location data. This location data is shared through service providers as well as mobile applications. Understanding how and with whom users share their location data as well as users’ perception of the underlying privacy risks, are important notions to grasp in order to design usable privacy-enhancing technologies. In this work, we perform a quantitative and qualitative analysis of smartphone users’ awareness, perception and self-reported behavior towards location data-sharing through a survey of n = 99 young adult participants (i.e., digital natives). We compare stated practices with actual behaviors to better understand their mental models, and survey participants’ understanding of privacy risks before and after the inspection of location traces and the information that can be inferred therefrom. Our empirical results show that participants have risky privacy practices: about 54% of participants underestimate the number of mobile applications to which they have granted access to their data, and 33% forget or do not think of revoking access to their data. Furthermore, most of the participants do not have a realistic perception of privacy risks and have generally heard little about privacy-related scandals. Also, by using a demonstrator to perform inferences from location data, we observe that slightly more than half of participants (57%) are surprised by the extent of potentially inferred information, and that 47% intend to reduce access to their data via permissions as a result of using the demonstrator. Last, a majority of participants have little knowledge of the tools to better protect themselves, but are nonetheless willing to follow suggestions to improve privacy (51%). Educating people, including digital natives, about privacy risks through transparency tools seems a promising approach.
Dynamics of Language Change: A Mixed-Methods Analysis of Language in an Online Transgender Community
Trans-affirming language can be critical for trans people in transphobic sociopolitical contexts. But what kinds of trans activism -- and whose -- has determined what language is considered trans-affirming? Collective negotiations of trans language norms have long occurred across online platforms, providing rich data to explore what language change looks like in a community where language is highly politicized. In this paper, we use a mixed-methods approach to explore patterns of language change in one popular early online transmasculine community on LiveJournal. Using bigram snapshot language models, we establish canonical patterns of community-level change in aggregate language usage. We dig into how these patterns relate to population turnover by analyzing distinct groups of users, extending existing quantitative analysis to disentangle group-level variation from community-level change and combining it with qualitative analysis that scrutinizes the interactional contexts of change. We find influence of linguistic, interactional, and identity norms that are negotiated and enforced by highly active users, thus driving language change. Our analysis demonstrates the role of power in community-internal language negotiation, with implications for trans language change more broadly.
Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks
Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event fingerprint, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.
Many social media studies argue that social media creates echo chambers where some users only interact with peers of the same political orientation. However, recent studies suggest that a substantial amount of Cross-Partisan Interactions (CPIs) do exist --- even within echo chambers, but they may be toxic. There is no consensus about how such interactions occur and when they lead to healthy or toxic dialogue. In this paper, we study a comprehensive Twitter dataset that consists of 3 million tweets from 2020 related to the U.S. context to understand the dynamics behind CPIs. We investigate factors that are more associated with such interactions, including how users engage in CPIs, which topics are more contentious, and what are the stances associated with healthy interactions. We find that CPIs are significantly influenced by the nature of the topics being discussed, with politically charged events acting as strong catalysts. The political discourse and pre-established political views sway how users participate in CPIs, but the direction in which users go is nuanced. While Democrats engage in cross-partisan interactions slightly more frequently, these interactions often involve more negative and nonconstructive stances compared to their intra-party interactions. In contrast, Republicans tend to maintain a more consistent tone across interactions. Although users are more likely to engage in CPIs with popular accounts in general, this is less common among Republicans who often engage in CPIs with accounts with a low number of followers for personal matters. Our study has implications beyond Twitter as identifying topics with low toxicity and high CPI can help highlight potential opportunities for reducing polarization while topics with high toxicity and low CPI may action targeted interventions when moderating harm.
LLMs Generate Structurally Realistic Social Networks but Overestimate Political Homophily
Generating social networks is essential for many applications, such as epidemic modeling and social simulations. The emergence of generative AI, especially large language models (LLMs), offers new possibilities for social network generation: LLMs can generate networks without additional training or need to define network parameters, and users can flexibly define individuals in the network using natural language. However, this potential raises two critical questions: 1) are the social networks generated by LLMs realistic, and 2) what are risks of bias, given the importance of demographics in forming social ties? To answer these questions, we develop three prompting methods for network generation and compare the generated networks to a suite of real social networks. We find that more realistic networks are generated with “local” methods, where the LLM constructs relations for one persona at a time, compared to “global” methods that construct the entire network at once. We also find that the generated networks match real networks on many characteristics, including density, clustering, connectivity, and degree distribution. However, we find that LLMs emphasize political homophily over all other types of homophily and significantly overestimate political homophily compared to real social networks.
A Study of Training Strategies on Enhancing Human Detection of AI-Synthesized Faces
Artificial intelligence (AI) synthesized faces—so called deepfake images—have been increasingly used for malicious intent and have resulted in prominently adverse impact. Because online users must contend with discerning fake from real, great emphasis has been placed on enhancing human detection of deepfake images. We conducted an online human-subject study (N=237), investigating the effect of three training strategies (explicit training with visible artifacts in synthetic faces, implicit training with experiencing the generation of synthetic faces using real human faces, and a combination of both artifact and generation) on participants’ detection of synthetic faces generated by the state-of-the-art StyleGAN techniques. Comparing participants’ deepfake detection across three phases (baseline in phase 1 without any training, phase 2 after one training session, and phase 3 after the other training session), we found that all training strategies effectively enhanced participants’ detection of AI-synthesized faces and their decision confidence. We also explored factors that impact participants’ learning and decision-making of deepfake detection. Responses to the open-ended question revealed that participants developed generalized strategies and utilized artifacts beyond the training. Our quantitative and qualitative results provide nuanced insights into the promises and limitations of the training strategies. In addition to advancing theoretical understanding of human training in the context of deepfake image detection, our study findings hold practical implications for interface design.
What Do You Meme? -- Identifying Characteristics and User Perceptions of Suicide Memes in Social Media
Suicide memes have recently attracted increasing attention from the public. The visual stimulus and the humorous nature of memes, which seem contradictory to serious suicide-related topics, lead to a lack of understanding of how this new expression differs from general serious suicide notes. Focusing on the r/SuicideMeme Reddit community, we present a computational quantitative study to better understand suicide memes. Our findings revealed users’ visual preferences for suicide memes: fewer and smaller faces, less colorful, sharper and higher contrast images, along with sadder, more fearful, angrier, and higher-arousal overlaying text. We found that these features impact user interactions to different degrees, with --- for example --- the existence of faces associated with a 10.6% decrease in the expected number of comments. Using topic modeling, we also discovered that user perceptions of suicide memes in r/SuicideMeme were mostly favourable. Further, we developed prediction models to identify suicide memes that achieved an accuracy of 80.04% and an F1 score of 80.75%. To the best of our knowledge, this is the first computational quantitative empirical study on suicide memes on social media. We believe our study provides a deeper understanding of the characteristics and motivations behind suicide memes, and insights for designing more effective moderation strategies for such content. Content Warning: This paper discusses the sensitive topic of suicide and contains suicide-related content.
Is Fact-Checking Politically Neutral? Asymmetries in How U.S. Fact-Checking Organizations Pick Up False Statements Mentioning Political Elites
Political elites play an important role in the proliferation of online misinformation. However, an understanding of how fact-checking platforms pick up politicized misinformation for fact-checking is still in its infancy. Here, we conduct an empirical analysis of mentions of U.S. political elites within fact-checked statements. For this purpose, we collect a comprehensive dataset consisting of 35,014 true and false statements that have been fact-checked by two major fact-checking organizations (Snopes, PolitiFact) in the U.S. between 2008 and 2023, i.e., within an observation period of 15 years. Subsequently, we perform content analysis and explanatory regression modeling to analyze how veracity is linked to mentions of U.S. political elites in fact-checked statements. Our analysis yields the following main findings: (i) Fact-checked false statements are, on average, 20% more likely to mention political elites than true fact-checked statements. (ii) There is a partisan asymmetry such that fact-checked false statements are 88.1% more likely to mention Democrats, but 26.5% less likely to mention Republicans, compared to fact-checked true statements. (iii) Mentions of political elites in fact-checked false statements reach the highest level during the months preceding elections. (iv) Fact-checked false statements that mention political elites carry stronger other-condemning emotions and are more likely to be pro-Republican, compared to fact-checked true statements. In sum, our study offers new insights into understanding mentions of political elites in false statements on U.S. fact-checking platforms, and bridges important findings at the intersection between misinformation and politicization.
Prevalence, Substance and Responses to Hate Speech Against LGBTQ Communities on TikTok
Despite ongoing efforts, online hate speech remains a pervasive issue on social media, particularly affecting vulnerable groups such as LGBTQ communities. While there is extensive debate around how best to address this problem, counter speech is emerging as a promising solution. However, existing research has primarily focused on detecting hateful content, often overlooking broader aspects such as the specific topics of discrimination and the spread of countermeasures online. This study examines the prevalence of hate speech and counter speech in LGBTQ online spaces on TikTok, analysing day-to-day interactions to identify recurring themes and targets. Results reveal that hate speech is widespread: at least 3.5% of messages contain hateful content, spread by approximately 4% of users, and one in three videos attracts hate comments or replies, primarily targeting LGBTQ topics explicitly. Gender identity emerges as a major focus, with transgender and non-binary individuals being frequent targets. Although much hate engagement goes unanswered, when responses occur, they are often in the form of counter speech, especially when LGBTQ-related topics are targeted. These findings improve our understanding of the nature and extent of online hate speech against LGBTQ communities, confirm counter speech as an employed response, and provide a foundation for further research aimed at developing strategies to promote safer, more inclusive social media environments.
The Emerging Generative Artificial Intelligence Divide in the United States
The digital divide refers to disparities in access to and use of digital tooling across social and economic groups. This divide can reinforce marginalization both at the individual level and at the level of places, because persistent economic advantages accrue to places where new technologies are adopted early. To what extent are emerging generative artificial intelligence (AI) tools subject to these social and spatial divides? We leverage a large-scale search query database to characterize U.S. residents' knowledge of a novel generative AI tool, ChatGPT, during its first six months of release. We identify hotspots of higher-than-expected search volumes for ChatGPT in coastal metropolitan areas, while coldspots are evident in the American South, Appalachia, and the Midwest. Nationwide, counties with the highest rates of search have proportionally more educated and more economically advantaged populations, as well as proportionally more technology and finance-sector jobs in comparison with other counties or with the national average. Observed associations with race/ethnicity and urbanicity are attenuated in fully adjusted hierarchical models, but education emerges as the strongest positive predictor of generative AI awareness. In the absence of intervention, early differences in uptake show a potential to reinforce existing spatial and socioeconomic divides.
Understanding Online Polarization Through Human-Agent Interaction in a Synthetic LLM-Based Social Network
The rise of social media has fundamentally transformed how people engage in public discourse and form opinions. While these platforms offer unprecedented opportunities for democratic engagement, they have been implicated in increasing social polarization and the formation of ideological echo chambers. Previous research has primarily relied on observational studies of social media data or theoretical modeling approaches, leaving a significant gap in our understanding of how individuals respond to and are influenced by polarized online environments. Here we present a novel experimental framework for investigating polarization dynamics that allows human users to interact with LLM-based artificial agents in a controlled social network simulation. Through a user study with 122 participants, we demonstrate that this approach can successfully reproduce key characteristics of polarized online discourse while enabling precise manipulation of environmental factors. Our results provide empirical validation of theoretical predictions about online polarization, showing that polarized environments significantly increase perceived emotionality and group identity salience while reducing expressed uncertainty. These findings extend previous observational and theoretical work by providing causal evidence for how specific features of online environments influence user perceptions and behaviors. More broadly, this research introduces a powerful new methodology for studying social media dynamics, offering researchers unprecedented control over experimental conditions while maintaining ecological validity.
Social Dynamics and Mobilization Potential of Online Election Narratives
The fragmentation of social media challenges how we might efficiently and effectively identify, understand, and counter harmful content. Prior work establishes frameworks for measuring problematic narratives, evaluating harms, and leveraging interdisciplinary theories and findings to design mitigating solutions. However, there is little understanding of these phenomena outside of mainstream platforms. This is particularly concerning given that alt-tech users have been observed to include insurrectionists, active shooters, and other extremists – often driven from mainstream platforms due to deplatforming and content moderation . Our work aims to characterize online narratives across alt-tech platforms. In particular, we highlight how rumoring and conspiracy theory narratives in the context of the 2022 U.S. elections impact social dynamics and inspire collective action. We gather a unique dataset of over 7,000 social media posts from Gab, Gettr, Parler, and Truth Social from which we derive prevalent narratives using natural language processing techniques. We then examine how the platform, affect, and engagement differ across context through the lens of narrative, social identity, and mobilization potential using mixed methods. Findings from our analyses show variation between how narratives support social identity conceptions of power and mobilization potential.
Cannabis Use During Pregnancy: Insights from Online Discourse and Socioeconomic Indicators Across the USA and Canada
Cannabis use is on the rise, driven by relaxing legal regulations and declining perceptions of harm. This trend, coupled with the increasing reliance on social media for health-related information, has sparked interest in cannabis use during pregnancy (CanPreg). This study examines online discourse about CanPreg on Twitter, analyzing 53,183 unique tweets from 32,744 users in the USA and Canada between 2012 and 2021. We investigate the spatio-temporal distribution of CanPreg discussions, key topical contexts within these conversations, and their correlations with socioeconomic and health indicators. The analysis reveals regional differences, with a relatively higher interest in CanPreg discussions in Canada compared to the USA. The online discourse is primarily focused on research, alongside criticism, personal experiences, queries, news sharing, and advertisements. Additionally, correlations between CanPreg tweet activity, poverty rates, and mental health metrics suggest a connection between online discussions and real-world behaviors. This study highlights the role of social media in health communication and provides insights to inform targeted intervention strategies.
Nicer than Humans: How Do Large Language Models Behave in the Prisoner's Dilemma?
The behavior of Large Language Models (LLMs) as artificial social agents is largely unexplored, and we still lack extensive evidence of how these agents react to simple social stimuli. Testing the behavior of AI agents in classic Game Theory experiments provides a promising theoretical framework for evaluating the norms and values of these agents in archetypal social situations. In this work, we investigate the cooperative behavior of three LLMs (Llama2, Llama3, and GPT3.5) when playing the Iterated Prisoner's Dilemma against random adversaries displaying various levels of hostility. We introduce a systematic methodology to evaluate an LLM's comprehension of the game rules and its capability to parse historical gameplay logs for decision-making. We conducted simulations of games lasting for 100 rounds and analyzed the LLMs' decisions in terms of dimensions defined in the behavioral economics literature. We find that all models tend not to initiate defection but act cautiously, favoring cooperation over defection only when the opponent's defection rate is low. Overall, LLMs behave at least as cooperatively as the typical human player, although our results indicate some substantial differences among models. In particular, Llama2 and GPT3.5 are more cooperative than humans, and especially forgiving and non-retaliatory for opponent defection rates below 30%. More similar to humans, Llama3 exhibits consistently uncooperative and exploitative behavior unless the opponent always cooperates. Our systematic approach to the study of LLMs in game theoretical scenarios is a step towards using these simulations to inform practices of LLM auditing and alignment.
A Growing Sense of Alienation: Spirals of Silence and Suppression of Structural Circumstances of Suicide in News
Suicide is a leading cause of death in the United States. Global safe reporting guidelines for news reports of suicide intend to mitigate associations of increased suicide incidence and stigma. However, recent research suggests more latent patterns in news beyond the guidelines could still contribute to suicide outcomes such as inhibited help-seeking and isolation. Using the Theory of Spiral of Silence to center isolation, we take a mixed-methods approach to analyze 22,021 articles (2020-2024) and use a zero-shot learning large language model (LLM) classifier to detect suppression of four structural circumstances of suicide: financial/job, legal, school, and access to physical/mental healthcare. We find that circumstance disclosure by news publishers diverges by political leaning, financial (p = 0.016), legal (p < 0.001), and school (p < 0.001); and by regionality, legal (p < 0.001) and health (p < 0.001). We qualify mechanisms of suppression using topic modeling and content sharing networks (CSNs). The spiral of silence lens highlights that left leaning publishers are more likely to disclose systemically or socially collective circumstances. In contrast, right leaning outlets suppress those and instead disclose instances that blame individuals for their experiences. Our work highlights how news reporting can downplay structural factors contributing to suicide. Content Warning: This paper discusses suicide deaths reported in news articles and may be sensitive to readers.
Disruptions in Music Listening Behaviors During Lockdowns
This study examines how individual music listening behaviors evolved during the COVID-19 lockdowns in France, focusing on both listening volumes and rhythms. We combine passively collected individual listening history data, provided by a music streaming service and covering the 2019-2023 period, with survey data collected from the same users (n ≈ 10000). Using the Dynamic Time Warping method, we develop a typology of listening trajectories during the first lockdown. The results reveal significant and heterogeneous changes in listening behavior, with approximately one-third of respondents experiencing a significant decrease in listening volume, while a quarter experienced an increase. We then analyze the evolution of the intervals between consecutive music listening sessions — so called inter-session times — to assess disruptions in individual listening rhythms. We uncover an unprecedented shift in the listening rhythms at the onset of first lockdown, reflecting varying degrees of disruption in daily life rhythms. For half of the individuals this disruption lasted more than four weeks. Finally we show that age, educational attainment and household structure unevenly influence the reorganization of music listening activity during this period, shedding light on the social differentiations at work in the reorganization of an ordinary activity during this crisis period.
Analyzing Reddit Stories of Sexual Violence: Incidents, Effects, and Requests for Advice
Warning: This paper may contain triggering language for some readers, especially survivors of sexual violence. Survivors of sexual violence sometimes share their experiences on social media, revealing their feelings and emotions and seeking advice. On platforms such as Reddit, some stories can be long---up to 40,000 characters. We posit that such long stories are demanding for helpers to read and respond to. Prior research has indicated that parts of these stories describing the incident, the effects on the poster, and advice requested by the poster are important. Highlighting those parts can draw helpers' attention toward key information and assist them in reading and responding to long stories. We first examine the stories posted on Reddit for the prevalence of these parts. Second, we develop a computational model to highlight these parts of a story. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute METHREE, a dataset comprising 8,947 labeled sentences for these parts from Reddit stories. A survey of users who are helpers on some relevant subreddits shows that the parts highlighted by our tool represent important information and assist them while reading and responding to long stories. We find that these tool-generated highlights statistically significantly reduce the demandingness of long stories. Moreover, almost all helpers felt that highlighted stories are helpful and easier to read, understand, and respond to than nonhighlighted ones. In particular, on a 4-point Likert scale, there is about 0.7 point reduction in demandingess when stories were presented with highlights.
Global Patterns of Viral Content on WhatsApp
This paper explores the nature and spread of viral WhatsApp content among everyday users in three diverse countries: India, Indonesia, and Colombia. By analyzing hundreds of viral messages collected with participants’ consent from private WhatsApp groups, we provide one of the first cross-cultural categorizations of viral content on WhatsApp. Despite the differences in cultural and geographic settings, our findings reveal striking similarities in the types of groups users engage with and the viral content they receive, particularly in the prevalence of misinformation. Our comparative analysis shows that viral content often includes political and religious narratives, with misinformation frequently recirculated despite prior debunking by fact-checking organizations. These parallels suggest that closed messaging platforms like WhatsApp facilitate similar patterns of information dissemination across different cultural contexts. This work contributes to the broader understanding of global digital communication ecosystems and provides a foundation for future research on information flow and moderation strategies in private messaging platforms.
Modeling Information Narrative Evolution on Telegram During the Russia-Ukraine War
Following the Russian Federation's full-scale invasion of Ukraine in February 2022, a multitude of information narratives emerged within both pro-Russian and pro-Ukrainian communities online. As the conflict progresses, so too do the information narratives, constantly adapting and influencing local and global community perceptions and attitudes. This dynamic nature of the evolving information environment (IE) underscores a critical need to fully discern how narratives evolve and affect online communities. Existing research, however, often fails to capture information narrative evolution, overlooking both the fluid nature of narratives and the internal mechanisms that drive their evolution. Recognizing this, we introduce a novel approach designed to both model narrative evolution and uncover the underlying mechanisms driving them. In this work we perform a comparative discourse analysis across communities on Telegram covering the initial three months following the invasion. First, we uncover substantial disparities in narratives and perceptions between pro-Russian and pro-Ukrainian communities. Then, we probe deeper into prevalent narratives of each group, identifying key themes and examining the underlying mechanisms fueling their evolution. Finally, we explore influences and factors that may shape the development and spread of narratives.
Fear and Loathing on the Frontline: Decoding the Language of Othering by Russia-Ukraine War Bloggers
Othering—the process of portraying an outgroup as fundamentally different and inferior—often escalates into framing the outgroup as an existential threat, thereby legitimizing exclusion and violence. Throughout history, othering has played a central role in conflicts, from genocides in Nazi Germany and Rwanda to contemporary hostility toward migrants in the US and Europe. Traditional computational methods, such as those used for hate speech detection, frequently overlook the subtle, context-dependent nature of othering language, limiting their effectiveness in real-time detection and analysis. Our work addresses these limitations through three key contributions: (1) a computational framework that combines sociological theory with large language models (LLMs) to identify and analyze othering language, (2) an in-depth examination of othering discourse dynamics, focusing on attention patterns and its interplay with moral framing, and (3) a rapid domain adaptation enabling robust analysis across different platforms and contexts. We apply our framework to a large corpus of Telegram messages from Russo-Ukrainian war bloggers and political discourse on Gab, revealing several previously unquantified patterns: othering rhetoric surges during crises, often intertwines with moralized language, and escalates during critical periods. Our findings demonstrate that this approach not only surpasses existing hate and fear speech detection methods but also offers actionable insights for anticipating and mitigating threats to social cohesion in conflict-prone environments.
Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models
Most web and digital trace data do not include information about an individual's nationality due to privacy concerns. The lack of data on nationality can create challenges for migration research. It can lead to a left-censoring issue since we are uncertain about the migrant's country of origin. Once we observe an emigration event, if we know the nationality, we can differentiate it from return migration. We propose methods to detect the nationality with the least available data, i.e., full names. We use the detected nationality in comparison with the country of academic origin, which is a common approach in studying the migration of researchers. We gathered 2.6 million unique name-nationality pairs from Wikipedia and categorized them into families of nationalities with three granularity levels to use as our training data. Using a character-based machine learning model, we achieved a weighted F1 score of 84% for the broadest- and 67%, for the most granular, country-level categorization. In our empirical study, we used the trained and tested model to assign nationality to 8+ million scholars' full names in Scopus data. Our results show that using the country of first publication as a proxy for nationality underestimates the size of return flows, especially for countries with a more diverse academic workforce, such as the USA, Australia, and Canada. We found that around 48% of emigration from the USA was return migration once we used the country of name origin in contrast to 33% based on academic origin. In the most recent period, 79% of scholars whose affiliation has consistently changed from the USA to China, and are considered emigrants, have Chinese names in contrast to 41% with a Chinese academic origin. Our proposed methods in addressing left-censoring issues are beneficial for other research that uses digital trace data to study migration.
Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets
The rise of online platforms exacerbated the spread of hate speech, demanding scalable and effective detection. However, the accuracy of hate speech detection systems heavily relies on human-labeled data, which is inherently susceptible to biases. While previous work has examined the issue, the interplay between the characteristics of the annotator and those of the target of the hate are still unexplored. We fill this gap by leveraging an extensive dataset with rich socio-demographic information of both annotators and targets, uncovering how human biases manifest in relation to the target's attributes. Our analysis surfaces the presence of widespread biases, which we quantitatively describe and characterize based on their intensity and prevalence, revealing marked differences. Furthermore, we compare human biases with those exhibited by persona-based LLMs. Our findings indicate that while persona-based LLMs do exhibit biases, these differ significantly from those of human annotators. Overall, our work offers new and nuanced results on human biases in hate speech annotations, as well as fresh insights into the design of AI-driven hate speech detection systems.
ClipMind: A Framework for Auditing Short-Format Video Recommendations Using Multimodal AI Models
We are witnessing a significant shift in social media platforms; we are transitioning from chronological social media feeds to feeds that are driven by AI recommendation systems. While the main goal of AI recommendation systems is to suggest engaging content to users, there are also some associated risks: AI recommendation systems can promote extreme content, causing negative consequences like online polarization and user radicalization. Overall, there is a pressing need to design powerful techniques that allow us to audit AI recommendation systems. Motivated by this, our work introduces ClipMind, a scalable and generalizable framework using advanced AI models to audit these recommendation algorithms on short-format video platforms like TikTok and YouTube Shorts. We demonstrate the merits of our framework by collecting social media feeds from TikTok. Our analysis shows that TikTok’s recommendation algorithm increasingly recommends similar videos when a user expresses interest in mainstream topics like Food and Beauty Care. On the other hand, by investigating niche interests (War and Mental Health), we find no evidence of informational rabbit holes of extreme content on TikTok. Our work contributes to efforts that leverage AI for social good, as our framework can be used by several interested stakeholders, including users, social media platforms, regulators, and researchers, to understand and audit video-based algorithmic recommendations.
Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends
Social media users and inauthentic accounts, such as bots, may coordinate in promoting their topics. Such topics may give the impression that they are organically popular among the public, even though they are astroturfing campaigns that are centrally managed. It is challenging to predict if a topic is organic or a coordinated campaign due to the lack of reliable ground truth. In this paper, we create such a ground truth by detecting the campaigns promoted by ephemeral astroturfing attacks. These attacks push any topic to Twitter’s (X) trends list by employing bots that tweet in a coordinated manner within a short period and then immediately delete their tweets. We also manually curate a dataset of organic Twitter trends. We then create engagement networks out of these datasets which can serve as a challenging testbed for graph classification task to distinguish between campaigns and organic trends. Engagement networks consist of users as nodes and edges indicate engagements (retweets, replies, and quotes) between users. We release the engagement networks for 179 campaigns and 135 non-campaigns, and also provide finer-grain labels to characterize the type of the campaigns and non-campaigns. Our dataset, LEN (Large Engagement Networks), in the URL below. In comparison to traditional graph classification datasets, which are small with tens of nodes and hundreds of edges at most, graphs in LEN are larger. The average size of a graph in LEN has ∼11K nodes and ∼23K edges. We show that state-of-the-art GNN methods give only mediocre results for campaign vs. non-campaign and campaign type classification on LEN. LEN offers a unique and challenging playfield for the graph classification problem. We believe that LEN will help advance the frontiers of graph classification techniques on large networks and also provide an interesting use case in terms of distinguishing coordinated campaigns and organic trends.
How Polarized are Online Conversations About Childhood?
2020 through 2023 were unusually tumultuous years for children in the United States, and children's welfare was prominent in political debate. Theories in moral psychology suggest that political parties would treat concerns for children using different moral frames, and that moral conflict might drive substantial polarization in discussions about children. However, such partisan frames may still differ very little if there is limited underlying disagreement about moral issues and everyday concerns in childhood when not explicitly referencing politics. We evaluate claims of universality and division in moral language using tweets from 2019-2023 linked to U.S. voter records, focusing on expressed morality. Our results show that mentions of children by Republicans and Democrats are usually similar, differing no more than mentions by women and men, and tend to contain no large differences in accompanying moral words. To the extent that mentions of children did differ across parties, these differences were constrained to topics polarized well before the pandemic -- and slightly heightened when co-mentioned with `kids' or `children'. These topics reflected a small fraction of conversations about children. Overall, polarization of online discussion around childhood appears to reflect escalated polarization on lines of existing partisan conflicts rather than concerns originating from new concerns about the welfare of children during and after the pandemic.
Hot Tweets and Cold Posts: Variation in US Congresspeople's Ideological Presentation on Twitter and Facebook Over Time
This work presents a novel observational study of US congresspeople’s link-based news-sharing behaviors and ideological presentations across Facebook and Twitter. By analyzing the web domains these politicians share, we estimate their political ideologies and measure ideological extremity across the political and social contexts of these platforms. Our findings show that these politicians present as more ideologically extreme on Facebook than they appear on Twitter, particularly among Democrats. However, this difference is relatively small compared to the ideological shift between a politician’s publicly funded official account and their campaign account—a shift that is roughly seven times larger. Finally, we observe that these changes are not uniform over time across parties; expressed polarization within the Democratic Party notably increased from 2013 to 2017 before stabilizing, while the Republican Party became markedly more polarized starting in 2020. While more research is needed to identify the specific affordances that contribute to more expressed polarization on Facebook and potential temporal dynamics between these platforms, this work highlights the limitations of studies that focus on single platforms and opens new avenues for future research into how differences across online social spaces may impact political polarization.
LIN: Latent Influence Network for Discovering Hidden Directed Influence Links on Social Media
In the current social media landscape, the study of influence propagation and consensus formation has gained prominence. While user interactions like retweeting are apparent, the underlying pathways of influence often remain hidden and complex. This study proposes a novel network called Latent Influence Network (LIN), which advances the analysis of influence on social media. LIN's architecture and the process of parameter selection are meticulously discussed within the comprehensive Latent Influence Detection Framework (LIDET). Based on the user's behavior label, LIN identifies the optimal network configuration, revealing more accurate influence patterns. We applied the LIDET framework to four diverse datasets, each demonstrating substantial improvements in influence pattern recognition over traditional network models. Specifically, in a case study on a COVID-19 dataset, LIN achieved a classification accuracy of 99%, significantly outperforming conventional methods. These findings underscore the utility of LIN in capturing the dynamics of influence and enhancing our understanding of opinion formation on social media.
What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation
Warning: This paper contains memes that may be offensive to some readers. Multimodal Internet Memes are now a ubiquitous fixture in online discourse. One strand of meme-based research is the classification of memes according to various affects, such as sentiment and hate, supported by manually compiled meme datasets. Understanding the unique characteristics of memes is crucial for meme classification. Unlike other user-generated content, memes spread via memetics, i.e. the process by which memes are imitated and transformed into elements used to create new memes. In effect, there exists an ever-evolving pool of visual and linguistic elements that underpin meme culture and are crucial to interpreting the meaning of individual memes. The current approach of training supervised learning models on static datasets, without taking memetics into account, limits the depth and accuracy of meme interpretation. We argue that meme datasets must contain genuine memes, as defined via memetics, so that effective meme classifiers can be built. In this work, we develop a meme identification protocol which distinguishes meme from non-memetic content by recognising the memetic element within it. We apply our protocol to a random sample of the leading 7 meme classification datasets and observe that more than half (50. 4%) of the evaluated samples were found to contain no signs of memetics. Our work also provides a meme typology grounded in memetics, providing the basis for more effective approaches to the interpretation of memes and the creation of meme datasets.
Contrastive Instruction Fine-Tuning Large Multimodal Model for Hateful Meme Classification
Detecting hateful memes requires a model that possesses extensive background knowledge and robust reasoning abilities, especially when the memes contain ambiguous descriptions. Previous research has used large language models (LLMs) and large multimodal models (LMMs) to interpret and categorize these memes. However, distinguishing subtly different hateful and non-hateful memes is still challenging. In recognition of this, our study introduces a unique contrastive instruction fine-tuning approach, InstructMemeCL. This method improves an LMM's ability to discern between memes that have similar visual or textual elements by intensifying its focus on semantic subtleties that separate hateful from non-hateful content. We evaluated our model using AUROC and accuracy metrics on three publicly available hateful meme datasets. The results indicate that our improved LMM more accurately identifies hateful and non-hateful memes, demonstrating superior performance compared to conventional LLMs and LMMs used in similar tasks.
Demystifying Hateful Content: Leveraging Large Multimodal Models for Hateful Meme Detection with Explainable Decisions
Hateful meme detection presents a significant challenge as a multimodal task due to the complexity of interpreting implicit hate messages and contextual cues within memes. Previous approaches have fine-tuned pre-trained vision-language models (PT-VLMs), leveraging the knowledge they gained during pre-training and their attention mechanisms to understand meme content. However, the reliance of these models on implicit knowledge and complex attention mechanisms renders their decisions difficult to explain, which is crucial for building trust in meme classification. In this paper, we introduce IntMeme, a novel framework that leverages Large Multimodal Models (LMMs) for hateful meme classification with explainable decisions. IntMeme addresses the dual challenges of improving both accuracy and explainability in meme moderation. The framework uses LMMs to generate human-like, interpretive analyses of memes, providing deeper insights into multimodal content and context. Additionally, it uses independent encoding modules for both memes and their interpretations, which are then combined to enhance classification performance. Our approach addresses the opacity and misclassification issues associated with PT-VLMs, optimizing the use of LMMs for hateful meme detection. We demonstrate the effectiveness of IntMeme through comprehensive experiments across three datasets, showcasing its superiority over state-of-the-art models.
The Peripatetic Hater: Predicting Movement Among Hate Subreddits
Many online hate groups exist to disparage others based on race, gender identity, sex, or other characteristics. The accessibility of these communities allows users to join multiple types of hate groups (e.g., a racist community and a misogynistic community), raising the question of whether users who join additional types of hate communities could be further radicalized compared to users who stay in one type of hate group. However, little is known about the dynamics of joining multiple types of hate groups, nor the effect of these groups on peripatetic users. We develop a new method to classify hate subreddits and the identities they disparage, then apply it to understand better how users come to join different types of hate subreddits. The hate classification technique utilizes human-validated deep learning models to extract the protected identities attacked, if any, across 168 subreddits. We find distinct clusters of subreddits targeting various identities, such as racist subreddits, xenophobic subreddits, and transphobic subreddits. We show that when users become active in their first hate subreddit, they have a high likelihood of becoming active in additional hate subreddits of a different category. We also find that users who join additional hate subreddits, especially those of a different category develop a wider hate group lexicon. These results then lead us to train a classification model that, as we demonstrate, usefully predicts the hate categories in which users will become active based on post text replied to and written. The accuracy of this model may be partly driven by peripatetic users often using the language of hate subreddits they eventually join. Overall, these results highlight the unique risks associated with hate communities on a social media platform, as discussion of alternative targets of hate may lead users to target more protected identities.
NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback
Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address digital media bias is to detect and indicate it automatically through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. Human-in-the-loop-based feedback mechanisms have proven an effective way to facilitate the data-gathering process. Therefore, we introduce and test feedback mechanisms for the media bias domain, which we then implement on NewsUnfold, a news-reading web application to collect reader feedback on machine-generated bias highlights within online news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, the feedback mechanism shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnfold demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses and continuously update datasets to changes in context.
Does the Source of a Warning Matter? Examining the Effectiveness of Veracity Warning Labels Across Warners
In this study, we conducted an online, between-subjects experiment (N = 2,049) to better understand the impact of warning label sources on information trust and sharing intentions. Across four warners (the social media platform, other social media users, Artificial Intelligence (AI), and fact checkers), we found that all significantly decreased trust in false information relative to control, but warnings from AI were modestly more effective. All warners significantly decreased the sharing intentions of false information, except warnings from other social media users. AI was again the most effective. These results were moderated by prior trust in media and the information itself. Most noteworthy, we found that warning labels from AI were significantly more effective than all other warning labels for participants who reported a low trust in news organizations, while warnings from AI were no more effective than any other warning label for participants who reported a high trust in news organizations.
Hate Speech Campaigns in the 2016 Philippine Elections on Facebook
The paper presents a comprehensive analysis of hate speech and trolling campaigns on Facebook during the 2016 national elections in the Philippines. Employing a vast dataset of hundreds of millions of Facebook comments, we uncover the first empirical evidence of coordinated online hate speech campaigns in this political context. Our findings reveal that over 12% of comments on political pages contained hate speech, predominantly originating from supporters of then-candidate Rodrigo Duterte and his affiliates. We further examine the relationship between offline political events and online hate speech, finding a surge in hateful commenting following the launch of Duterte’s campaign, though similar spikes were not observed after some of his later controversial remarks. Alarmingly, we observe a “spillover effect”: regular social media users, after exposure to orchestrated hate speech by highly active “troll” accounts, began emulating these behaviors. This contagion effect highlights a worrying trend in which hate speech normalizes and spreads within online communities. Overall, our results shed light on the dynamics of digital political campaigns and their implications for democracy and public discourse. Given Facebook’s ubiquity in the Philippines, these findings raise significant concerns about social media’s influence on electoral politics and the health of online civic dialogue.
Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs
Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "political discourse surrounding climate policies," etc. In this paper, we introduce a novel approach to uncovering latent themes in social media messaging. Recognizing the limitations of the traditional topic-level analysis, which tends to capture only overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Traditional theme discovery methods typically involve manual processes and a human-in-the-loop approach. While valuable, these methods face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of large language models (LLMs). This approach facilitates a deeper investigation into the social media discourse, revealing a variety of themes with distinct characteristics and relevance. It provides a detailed understanding of underlying nuances and efficiently maps texts to these themes, enhancing our insight into social media messaging. To demonstrate our approach, we apply our framework to contentious topics, such as climate debate and vaccine debate. We use two publicly available datasets: (1) the climate campaigns dataset of 21k Facebook ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our quantitative and qualitative analysis shows that our methodology yields more accurate and interpretable results compared to the baselines. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.
Please Say "Shibboleth": Socialization Through Language Adoption in Virtual Citizen Science
Socialization is crucial for open collaboration projects as incumbent participants establish and build relationships within their community. However, socialization often excludes engagement in a group's communicative and language practices. This study examines the socialization of newcomers to the communicative and language practices of Gravity Spy. Participants classify image subjects in Gravity Spy to isolate noise signals in gravitational wave detectors. Analyzing the discussion posts of Gravity Spy newcomers, we determine how they assimilate into the group's communicative practices. We find that socialization in a group’s language practices can be a valuable metric for integration. Our results suggest successful socialization is marked by the convergence of newcomers’ language with the community, influencing retention and participation. These findings provide insights into the linguistic dynamics of virtual citizen science communities and propose strategies for enhancing newcomer integration through language adoption.
Exploring Disparity-Accuracy Trade-offs in Face Recognition Systems: The Role of Datasets, Architectures, and Loss Functions
Automated Face Recognition Systems (FRSs), developed using deep learning models, are deployed worldwide for identity verification and facial attribute analysis. The performance of these models is determined by a complex interdependence among the model architecture, optimization/loss function and datasets. Although FRSs have surpassed human-level accuracy, they continue to be disparate against certain demographics. Due to the ubiquity of applications, it is extremely important to understand the impact of the three components-- model architecture, loss function and face image dataset on the accuracy-disparity trade-off to design better, unbiased platforms. In this work, we perform an in-depth analysis of three FRSs for the task of gender prediction, with various architectural modifications resulting in ten deep-learning models coupled with four loss functions and benchmark them on seven face datasets across 266 evaluation configurations. Our results show that all three components have an individual as well as a combined impact on both accuracy and disparity. We identify that datasets have an inherent property that causes them to perform similarly across models, independent of the choice of loss functions. Moreover, the choice of dataset determines the model's perceived bias-- the same model reports bias in opposite directions for three gender-balanced datasets of ``in-the-wild'' face images of popular individuals. The facial embeddings show that the models are unable to generalize a uniform definition of what constitutes a ``female face'' as opposed to a ``male face'', due to dataset diversity. We provide recommendations to model developers on using our study as a blueprint for development and subsequent deployment.
What’s in a Label? Propaganda Labels and User Sharing Behavior on Social Media Platforms
Authentic information is vital for a society's ability to make rational decisions. Fabricated and manipulative information can be harmful to society as seen in cases of threatening events that were consequences of foreign propaganda and radical ideologies. While past research has studied dis- and misinformation on social media platforms, the study of propaganda has received much less attention. This study explores the sharing intentions of propaganda on social media platforms and develops an intervention to help detect it. In a randomized controlled trial setting, we added indicators to social media posts that used propaganda techniques to advance an agenda, including techniques that rely on fallacious reasoning, emotional rather than logical reasoning, etc. We then asked our participants (n=1,187) about their intention to engage with these posts. We found that participants were significantly (2.4 times) less likely to share these posts with indicators. We also found that participants’ political affiliation moderated their sharing intentions. We believe our findings provide valuable insights for the study of propaganda on social media platforms.
Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africa
Despite being an integral tool for finding health-related information online, YouTube has faced criticism for disseminating COVID-19 misinformation globally to its users. Yet, prior audit studies have predominantly investigated YouTube within the Global North contexts, often overlooking the Global South. To address this gap, we conducted a comprehensive 10-day geolocation-based audit on YouTube to compare the prevalence of COVID-19 misinformation in search results between the United States (US) and South Africa (SA), the countries heavily affected by the pandemic in the Global North and the Global South, respectively. For each country, we selected 3 geolocations and placed sock-puppets, or bots emulating "real" users, that collected search results for 48 search queries sorted by 4 search filters for 10 days, yielding a dataset of 915K results. We found that 31.55% of the top-10 search results contained COVID-19 misinformation. Among the top-10 search results, bots in SA faced significantly more misinformative search results than their US counterparts. Overall, our study highlights the contrasting algorithmic behaviors of YouTube search between two countries, underscoring the need for the platform to regulate algorithmic behavior consistently across different regions of the Globe. Warning: We caution the readers that some examples provided to better contextualize our data can be offensive.
Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation
With the massive surge in ML models on platforms like Hugging Face, users often lose track and struggle to choose the best model for their downstream tasks, frequently relying on model popularity indicated by download counts, likes, or recency. We investigate whether this popularity aligns with actual model performance and how the comprehensiveness of model documentation correlates with both popularity and performance. In our study, we evaluated a comprehensive set of 500 Sentiment Analysis models on Hugging Face. This evaluation involved massive annotation efforts, with human annotators completing nearly 80,000 annotations, alongside extensive model training and evaluation. Our findings reveal that model popularity does not necessarily correlate with performance. Additionally, we identify critical inconsistencies in model card reporting: approximately 80% of the models analyzed lack detailed information about the model, training, and evaluation processes. Furthermore, about 88% of model authors overstate their models' performance in the model cards. Based on our findings, we provide a checklist of guidelines for users to choose good models for downstream tasks.
Post-GPT Policy: Risk and Regulation in EU AI Discourse
The ubiquity of artificial intelligence (AI) systems across society has generated discussion around risks posed by general-purpose AI (GPAI) technologies. At the same time, harmonized AI regulation is being devised in the European Union (EU) to ensure responsible AI development and uptake. This paper presents a mixed-methods study combining approaches from natural language processing (NLP) and qualitative content analysis to study shifting risk discourse in EU policy-related documents and media articles. The research is anchored around two recent events in the AI space: the popularization of GPAI following the release of ChatGPT and the introduction of the EU AI Act (AIA) regulation. The findings reveal a discursive emphasis on cybersecurity, regional imbalance of computational resources, education and AI literacy, and the implications of risks occurring at scale.
From Fake News to Real Protests: WhatsApp’s Role in Brazilian Political Coordination
The growth of social networks has raised concerns about the misuse of these platforms by disinformation campaigns, social bots, and coordinated activities. Among these platforms, WhatsApp has become a focal point for this abuse, particularly in Brazil, one of the countries with the highest use of the platform. Despite acknowledging the presence of coordinated campaigns and implementing restrictions on the number of messages forwarded per user, the platform continues to be abused. Due to its private nature and the difficulty of collecting information, little is known about these campaigns and the messages they disseminate. Given this context, our study investigates the presence of coordinated activities on WhatsApp in Brazil, identifying their content and purpose, especially how these messages relate to recent Brazilian political events. To answer these questions, we analyzed 13 million messages from 1,444 political groups over seven months from July 2022 to January 2023. Using network analysis, our findings suggest a significant prevalence of coordinated activity in the propagation of news messages, 26% of which originate from misinformation sites. Furthermore, we found that images play a key role in coordinated activity, accounting for 15% of messages, which are also used to mislead. Finally, coordinated accounts were used to organize collective actions, including attacks and protests against election results.
Capturing Dynamics in Online Public Discourse: A Case Study of Universal Basic Income Discussions on Reddit
Societal change is often driven by shifts in public opinion. As citizens evolve in their norms, beliefs, and values, public policies change too. While traditional opinion polling and surveys can outline the broad strokes of whether public opinion on a particular topic is changing, they usually cannot capture the full multidimensional richness of opinion present in a large heterogeneous population. However, an increasing fraction of public discourse about public policy issues is now occurring on online platforms, which presents an opportunity to measure public opinion change at a qualitatively different scale of resolution and context. In this paper, we present a conceptual model of observed opinion change on online platforms and apply it to study public discourse on Universal Basic Income (UBI) on Reddit throughout its history. UBI is a periodic, no-strings-attached cash payment given to every citizen of a population. We study UBI as it is a clearly-defined policy proposal that has recently experienced a surge of interest through trends like automation and events like the COVID-19 pandemic. We find that overall stance towards UBI on Reddit significantly declined until mid-2019, when this historical trend suddenly reversed and Reddit became substantially more supportive. Using our model, we find the most significant drivers of this overall stance change were shifts within different user cohorts, within communities that represented similar affluence levels, and within communities that represented similar partisan leanings. Our method identifies nuanced social drivers of opinion change in the large-scale public discourse that now regularly occurs online, and could be applied to a broad set of other important issues and policies.
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models
The advent of social media has given rise to numerous ethical challenges, with hate speech among the most significant concerns. Researchers are attempting to tackle this problem by using hate-speech detection and employing language models to automatically moderate content and promote civil discourse. Unfortunately, recent studies have revealed that hate-speech detection systems can be misled by adversarial attacks, raising concerns about their resilience. While previous research has separately addressed the robustness of these models under adversarial attacks and their explainability, there has been no comprehensive study exploring their intersection. The novelty of our work lies in combining these two critical aspects, leveraging explainability to identify potential vulnerabilities and enabling the design of targeted adversarial attacks. This paper quantifies the interplay between explainability and adversarial robustness in hate-speech detection models. We define novel metrics based on explainability-driven adversarial attacks to evaluate this relationship, providing a clear assessment of model vulnerabilities and guiding the development of more resilient systems.
Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization
The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across a range of online ideological community forums. By focusing on verbal behavioral signatures of extremist traits, we develop a framework for quantifying extremism at both user and community levels. Our research identifies 11 distinct factors, which we term "The Extremist Eleven," as a generalized psychosocial model of extremism. Applying our method to various online communities, we demonstrate an ability to characterize ideologically diverse communities across the 11 extremist traits. We demonstrate the power of this method by analyzing user histories from members of the incel community. We find that our framework accurately predicts which users join the incel community up to 10 months before their actual entry with an AUC of > 0.6, steadily increasing to AUC ~ 0.9 three to four months before the event. Further, we find that upon entry into an ideological forum, the users tend to maintain their level of extremist traits within the community, while still remaining distinguishable from the general online discourse. Our findings contribute to the study of extremism by introducing a more holistic, cross-ideological approach that transcends traditional, trait-specific models.
Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit
Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reasons including stigma faced by people who use opioids, online communities for recovery and support were formed on different social media platforms. In these communities, people share their experiences and solicit information by asking questions to learn about opioid use and recovery. These communities however do not always contain clinically verified information. In this paper, we study natural language questions asked in the context of OUD-related discourse on Reddit. We adopt transformer-based question detection along with hierarchical clustering across 19 subreddits to identify six coarse-grained categories and 69 fine-grained categories of OUD-related questions. Our analysis uncovers ten areas of information-seeking from Reddit users in the context of OUD: drug sales, specific drug-related questions, OUD treatment, drug uses, side effects, withdrawal, lifestyle, drug testing, pain management and others, during the study period of 2018-2021. Our work provides a major step in improving the understanding of OUD-related questions people ask unobtrusively on Reddit. We finally discuss technological interventions and public health harm reduction techniques based on the topics of these questions.
Partisan Fact-Checkers' Warnings Can Effectively Correct Individuals' Misbeliefs About Political Misinformation
Political misinformation, particularly harmful when it aligns with individuals' preexisting beliefs and political ideologies, has become widespread on social media platforms. In response, platforms like Facebook and X introduced warning messages leveraging fact-checking results from third-party fact-checkers to alert users against false content. However, concerns persist about the effectiveness of these fact-checks, especially when fact-checkers are perceived as politically biased. To address these concerns, this study presents findings from an online human-subject experiment (N=216) investigating how the political stances of fact-checkers influence their effectiveness in correcting misbeliefs about political misinformation. Our findings demonstrate that partisan fact-checkers can decrease the perceived accuracy of political misinformation and correct misbeliefs without triggering backfire effects. This correction is even more pronounced when the misinformation aligns with individuals' political ideologies. Notably, while previous research suggests that fact-checking warnings are less effective for conservatives than liberals, our results suggest that explicitly labeled partisan fact-checkers, positioned as political counterparts to conservatives, are particularly effective in reducing conservatives' misbeliefs toward pro-liberal misinformation.
Reddit Rules and Rulers: Quantifying the Link Between Rules and Perceptions of Governance Across Thousands of Communities
Rules are a critical component of the functioning of nearly every online community, yet it is challenging for community moderators to make data-driven decisions about what rules to set for their communities. The connection between a community's rules and how its membership feels about its governance is not well understood. In this work, we conduct the largest-to-date analysis of rules on Reddit, collecting a set of 67,545 unique rules across 5,225 communities which collectively account for more than 67% of all content on Reddit. More than just a point-in-time study, our work measures how communities change their rules over a 5+ year period. We develop a method to classify these rules using a taxonomy of 17 key attributes extended from previous work. We assess what types of rules are most prevalent, how rules are phrased, and how they vary across communities of different types. Using a dataset of communities' discussions about their governance, we are the first to identify the rules most strongly associated with positive community perceptions of governance: rules addressing who participates, how content is formatted and tagged, and rules about commercial activities. We conduct a longitudinal study to quantify the impact of adding new rules to communities, finding that after a rule is added, community perceptions of governance immediately improve, yet this effect diminishes after six months. Our results have important implications for platforms, moderators, and researchers. We make our classification model and rules datasets public to support future research on this topic.
The Susceptibility Paradox in Online Social Influence
Understanding susceptibility to online influence is crucial for mitigating the spread of misinformation and protecting vulnerable audiences. This paper investigates susceptibility to influence within social networks, focusing on the differential effects of influence-driven versus spontaneous behaviors on user content adoption. Our analysis reveals that influence-driven adoption exhibits high homophily, indicating that individuals prone to influence often connect with similarly susceptible peers, thereby reinforcing peer influence dynamics, whereas spontaneous adoption shows significant but lower homophily. Additionally, we extend the Generalized Friendship Paradox to influence-driven behaviors, demonstrating that users' friends are generally more susceptible to influence than the users themselves, de facto establishing the notion of Susceptibility Paradox in online social influence. This pattern does not hold for spontaneous behaviors, where friends exhibit fewer spontaneous adoptions. We find that susceptibility to influence can be predicted using friends' susceptibility alone, while predicting spontaneous adoption requires additional features, such as user metadata. These findings highlight the complex interplay between user engagement and characteristics in spontaneous content adoption. Our results provide new insights into social influence mechanisms and offer implications for designing more effective moderation strategies to protect vulnerable audiences.
Balancing Producer Fairness and Efficiency via Prior-Weighted Rating System Design
Online marketplaces use rating systems to promote the discovery of high-quality products. However, these systems also lead to high variance in producers' economic outcomes: a new producer who sells high-quality items, may unluckily receive a low rating early, severely impacting their future popularity. We investigate the design of rating systems that balance the goals of identifying high-quality products (``efficiency'') and minimizing the variance in outcomes of producers of similar quality (individual ``producer fairness''). We show that there is a trade-off between these two goals: rating systems that promote efficiency are necessarily less individually fair to producers. We introduce prior-weighted rating systems as an approach to managing this trade-off. Informally, the system we propose sets a system-wide prior for the quality of an incoming product; subsequently, the system updates that prior to a posterior for each product's quality based on user-generated ratings over time. We show theoretically that in markets where products accrue reviews at an equal rate, the strength of the rating system's prior determines the operating point on the identified trade-off: the stronger the prior, the more the marketplace discounts early ratings data (increasing individual fairness), but the slower the platform is in learning about true item quality (so efficiency suffers). We further analyze this trade-off in a responsive market where customers make decisions based on historical ratings. Through calibrated simulations in 19 different real-world datasets sourced from large online platforms, we show that the choice of prior strength mediates the same efficiency-consistency trade-off in this setting. Overall, we demonstrate that by tuning the prior as a design choice in a prior-weighted rating system, platforms can be intentional about the balance between efficiency and producer fairness.
Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing
Social media platforms provide users with a profile description field, commonly known as a "bio," where they can present themselves to the world. A growing literature shows that text in these bios can improve our understanding of online self-presentation and behavior, but existing work relies exclusively on keyword-based approaches to do so. We here propose and evaluate a suite of simple, effective, and theoretically motivated approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We evaluate our methods on four tasks, showing that the strongest one out-performs several practical baselines. We then show the utility of our method in helping understand associations between self-presentation and the sharing of URLs from low-quality news sites on Twitter, with a particular focus on explore the interactions between age and partisanship, and exploring the effects of self-presentations of religiosity. Our work provides new tools to help computational social scientists make use of information in bios, and provides new insights into how misinformation sharing may be perceived on Twitter.
Using Causality to Infer Coordinated Attacks in Social Media
The rise of social media has been accompanied by a dark side with the ease of creating fake accounts and disseminating misinformation through coordinated attacks. Existing methods to identify such attacks often rely on thematic similarities or network-based approaches, overlooking the intricate causal relationships that underlie coordinated actions. This work introduces a novel approach for detecting coordinated attacks using Convergent Cross Mapping (CCM), a technique that infers causality from temporal relationships between user activity. We build on the theoretical framework of CCM by incorporating topic modelling as a basis for further optimizing its performance. We apply CCM to real-world data from the infamous IRA attack on US elections, achieving F1 scores up to 75.3% in identifying coordinated accounts. Furthermore, we analyse the output of our model to identify the most influential users in a community and uncover leader-follower dynamics based on inferred causal relationships. We also demonstrate how our method reveals coordinated behaviour across different time periods, including campaigns predating the 2016 elections. We apply our model to a case study involving COVID-19 anti-vax related discussions on Twitter. Our results demonstrate the effectiveness of our model in uncovering causal structures of coordinated behaviour, offering a promising avenue for mitigating the threat of malicious campaigns on social media platforms.
LLM-Based Semantic Augmentation for Harmful Content Detection
Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-rich explanations, thereby enhancing training sets without substantial increases in data volume. We systematically evaluate on the SemEval 2024 multi-label Persuasive Meme dataset and further validate on the Google Jigsaw toxic comments and Facebook hateful memes datasets to assess generalizability. Our results reveal that zero-shot LLM classification underperforms on these high-context tasks compared to supervised models. In contrast, integrating LLM-based semantic augmentation yields performance on par with approaches that rely on human-annotated data, at a fraction of the cost. These findings underscore the importance of strategically incorporating LLMs into machine learning (ML) pipeline for social media classification tasks, offering broad implications for combating harmful content online. Disclaimer: This paper contains examples of explicit language that may be disturbing to some readers.
A Sticker is Worth a Thousand Words: Characterizing the Use and Abuse of Stickers on WhatsApp Political Groups in Brazil
Instant messaging platforms have become an important means of communication in our world. According to WhatsApp, more than 100 billion messages are sent daily through the app. Communication on these platforms has allowed individuals to express themselves in other types of media, rather than simple text, including audio, videos, images, and, more recently, stickers. This new multimedia format, in particular, emerged with messaging apps and gained considerable popularity among users, promoting new forms of interactions. Stickers range from static images of memes and emojis to animated images similar to GIFs, often used in humorous contexts. However, in the Brazilian context of WhatsApp, they are transcending their role as a mere form of humor to become an important element in political strategy. In this regard, we investigate how stickers are used, revealing unique characteristics that these media bring to public WhatsApp groups and, more specifically, the political use of this new media format. Furthermore, we found evidence of sticker abuse on WhatsApp, where users attack political opponents and spread hate speech and offensive content in public groups without any moderation. To investigate this phenomenon, we collected a large sample of messages from public political WhatsApp groups in Brazil and analyzed the sticker messages shared in this context. Warning! This paper contains images and terms that may be offensive to some audiences.
Online Myths on Opioid Use Disorder: A Comparison of Reddit and Large Language Model
Online communities on Reddit are a popular choice among people with opioid use disorder (OUD) to seek information on drug use, withdrawal symptoms, and recovery. LLM-powered chatbots (e.g., ChatGPT) are widely being adopted as question-answer systems for health-related queries. However, such online health information seeking could potentially be hindered by myths and misinformation on OUD, misleading or causing genuine harm to people with OUD. In this work, we examine the prevalence of 5 OUD-related myths, on treatment models and patient characteristics, within human- (taken from Reddit) and LLM-generated responses to queries on OUD. We further explore the framing strategies used within responses (both human- and LLM-generated) promoting and countering the myths. We found that all 5 myths were more widespread within human-generated responses. In addition, myth-promoting responses adopted trustworthy and authoritative framings, compared to knowledge-imparting linguistic cues within those countering the myths. Our work offers recommendations to reduce online OUD misinformation.
Successful Rhetorics: How Do Linguistic Dimensions Affect User Engagement with Different News Categories on Twitter?
This paper analyzes how different rhetorical attributes in news tweets, specifically analytical, clout, perceptual, and risk language, influence user engagement across publishers with different bias and reliability ratings. Using the LIWC framework to quantify these linguistic dimensions in a 5.5 million tweets dataset covering 1,553 news publishers and capturing over 480 million tweet interactions, we perform and present a category-based analysis that captures the relative impact that such features have on the user engagement rates associated with different political bias and reliability categories. While highly biased and unreliable publishers saw increased engagement for clout and risk language, confirming audience biases, the least biased ones benefited more from analytical language. Perception language, on the other hand, uniformly reduced engagement. These insights not only further our understanding of persuasion tactics but also have implications for curbing misinformation by aligning recommendations with audience veracity and impartiality preferences.
Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages
The advent of generative AI tools has had a profound impact on societies globally, transcending geographical boundaries. Understanding these tools' global reception and utilization is crucial for service providers and policymakers in shaping future policies. Therefore, to unravel the perceptions and engagements of individuals within diverse linguistic communities with regard to generative AI tools, we extensively analyzed over 6.8 million tweets in 14 different languages. Our findings reveal a global trend in the perception of generative AI, accompanied by language-specific nuances. While sentiments toward these tools vary significantly across languages, there is a prevalent positive inclination toward Image tools and a negative one toward Chat tools. Notably, the ban of ChatGPT in Italy led to a sentiment decline and initiated discussions across languages. Furthermore, we established a taxonomy for interactions with chatbots, creating a framework for social analysis underscoring variations in generative AI usage among linguistic communities. We find that the Chinese community predominantly employs chatbots as substitutes for search, while the Italian community tends to use chatbots for tasks such as problem-solving assistance and engaging in entertainment or creative tasks. Our research provides a robust foundation for further explorations of the social dynamics surrounding generative AI tools and offers invaluable insights for decision-makers in policy, technology, and education.
Written for Lawyers or Users? Mapping the Complexity of Community Guidelines
Recent regulatory efforts such as the EU's Digital Services Act aim to increase transparency in mostly opaque content moderation practices of social media platforms. They encourage platforms to post information about what content is prohibited on the platform. But what kinds of platforms follow these best practices, and how readable is the information posted for average users? This paper introduces the Content Moderation Policies and Reports Dataset (COMPARE), a new data collection comprising content moderation policies of 132 of the most popular social media platforms for comparative analysis. We then use COMPARE to measure the complexity of 89 community guidelines, focusing on length, readability, and semantic complexity. We find that while the largest platforms are most likely to make guidelines available, they also tend to have the longest and semantically most complex guidelines. In addition, community guidelines seem to grow in length with new regulation. Our results suggest that it is crucial to look beyond the mere provision of community guidelines and to study how complex guidelines are for users.
Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification
Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racial to national and gender biases. It remains uncertain whether these biases impact the performance of LLMs for certain tasks. In this study, we investigate the political biases of LLMs within the stance classification task, specifically examining whether these models exhibit a tendency to more accurately classify politically-charged stances. Utilizing three datasets, seven LLMs, and four distinct prompting schemes, we analyze the performance of LLMs on politically oriented statements and targets. Our findings reveal a statistically significant difference in the performance of LLMs across various politically oriented stance classification tasks. Furthermore, we observe that this difference primarily manifests at the dataset level, with models and prompting schemes showing statistically similar performances across different stance classification datasets. Lastly, we observe that when there is greater ambiguity in the target the statement is directed towards, LLMs have poorer stance classification accuracy.
Supporters and Skeptics: LLM-Based Analysis of Engagement with Mental Health (Mis)Information Content on Video-Sharing Platforms
Over one in five adults in the US lives with a mental illness. In the face of a shortage of mental health professionals and offline resources, online short-form video content has grown to serve as a crucial conduit for disseminating mental health help and resources. However, the ease of content creation and access also contributes to the spread of misinformation, posing risks to accurate diagnosis and treatment. Detecting and understanding engagement with such content is crucial to mitigating their harmful effects on public health. We perform the first quantitative study of the phenomenon using YouTube Shorts and Bitchute as the sites of study. We contribute MentalMisinfo, a novel labeled mental health misinformation (MHMisinfo) dataset of 739 videos (639 from Youtube and 100 from Bitchute) and 135372 comments in total, using an expert-driven annotation schema. We first found that few-shot in-context learning with large language models (LLMs) are effective in detecting MHMisinfo videos. Next, we discover distinct and potentially alarming linguistic patterns in how audiences engage with MHMisinfo videos through commentary on both video-sharing platforms. Across the two platforms, comments could exacerbate prevailing stigma with some groups showing heightened susceptibility to and alignment with MHMisinfo. We discuss technical and public health-driven adaptive solutions to tackling the ``epidemic'' of mental health misinformation online.
Toxic Bias: Perspective API Misreads German as More Toxic
Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.
Towards Characterizing and Detecting Incentivized Reviews on eCommerce Platforms
Customer reviews play an important role in rankings and visibility on e-commerce sites, and also strongly influence a customer's decision to purchase a product. Motivated by this, malicious sellers engage in incentivized review fraud to inflate their product ratings by providing customers with free products in exchange for five-star reviews, thus compromising review integrity. While there is ample prior work on fake reviews in general, there is limited prior work on incentivized review fraud. In this work, we infiltrate an underground market for fake reviews and implement a custom crawler to collect a dataset of malicious products that seek incentivized reviews. We devise and extract a set of features, and show that these are statistically significant in differentiating between benign and malicious products. Using hypothesis testing, we identify characteristics and trends exhibited by malicious products. While we are unable to achieve a high precision when we train standard machine learning models without compromising on the recall, we propose a lightweight two-phase technique that combines high-precision product classifiers with high-recall review classifiers. This technique allows us to minimize the false positives, with only a slight increase in false negatives. Finally, we also audit the effectiveness of two publicly available tools for incentivized review detection and find that they are not reliable. In summary, we contribute a new high-fidelity dataset, characterize products seeking incentivized reviews, audit existing tools for review analysis, and present a superior method for detecting review fraud. We hope that this research could be useful for e-commerce companies and other entities who have a stake in preserving opinion and review integrity online.
Analyzing Offensive Content and Emotional Dynamics in Black Lives Matter Discourse on Twitter
The Black Lives Matter (BLM) movement seeks to spread awareness and fight against social and racial injustice. In 2020, BLM-related discussions surged on social media after the death of George Floyd and the protests that followed. Previous works have qualitatively analyzed the scaling, dynamics, and topics of BLM discussions on social media. However, very few works have studied the offensive content, the emotions expressed, and the topics of offensive discussions in BLM-related discussions. In this measurement study, to examine offensive language and emotion, we conduct a largescale study of BLM discussions on Twitter. We first develop a classifier that uses sentiment representation to aid offensive language detection. We then develop an emotion classifier based on deep attention fusion with sentiment features to classify emotions. We further use topic modeling to analyze the topics of offensive tweets. Our analysis of over 20 million tweets revealed that offensive tweets peeked in the weeks following George Floyd’s death and rapidly decreased but remained stable. The analysis further revealed that negative emotions were the most expressed emotions. Offensive reply network analysis reveals that most offensive replies are unidirectional. Our contribution in this work is five-fold: (1) We identify offensive content during BLM protests; (2) we identify online emotions that were significant in the offensive and non-offensive content during the protests; (3) we assess the characteristics of users who replied offensively and those who are the recipients of offensive content; (4) we assess emotion dynamics across offenders and recipients; (5) we identify the hot topics that most drove the offensive content on Twitter. Our work offers important implications for content moderation and the conscious and unconscious attitudes towards the black/African American community.
Large Language Model Annotation Bias in Hate Speech Detection
Large language models (LLMs) are fast becoming ubiquitous and have shown impressive performance in various natural language processing (NLP) tasks. Annotating data for downstream applications is a resource-intensive task in NLP. Recently, the use of LLMs as a cost-effective data annotator for annotating data used to train other models or as an assistive tool has been explored. Yet, little is known regarding the societal implications of using LLMs for data annotation. In this work, focusing on hate speech detection, we investigate how using LLMs such as GPT-4 and Llama-3 for hate speech detection can lead to different performances for different text dialects and racial bias in online hate detection classifiers. We used LLMs to predict hate speech in seven hate speech datasets and trained classifiers on the LLM annotations of each dataset. Using tweets written in African-American English (AAE) and Standard American English (SAE), we show that classifiers trained on LLM annotations assign tweets written in AAE to negative classes (e.g., hate, offensive, abuse, racism, etc.) at a higher rate than tweets written in SAE and that the classifiers have a higher false positive rate towards AAE tweets. We explore the effect of incorporating dialect priming in the prompting techniques used in prediction, showing that introducing dialect increases the rate at which AAE tweets are assigned to negative classes.
Scaled Supervision is an Implicit Lipschitz Regularizer
In modern social media, recommender systems (RecSys) rely on the click-through rate (CTR) as the standard metric to evaluate user engagement. CTR prediction is traditionally framed as a binary classification task to predict whether a user will interact with a given item. However, this approach overlooks the complexity of real-world social modeling, where user, item, and their interactive features change dynamically in fast-paced online environments. This dynamic nature often leads to model instability, reflected in overfitting short-term fluctuations rather than higher-level interactive patterns. While overfitting calls for more scaled and refined supervisions, current solutions often rely on binary labels that overly simplify fine-grained user preferences through the thresholding process, which significantly reduces the richness of the supervision. Therefore, we aim to alleviate the overfitting problem by increasing the supervision bandwidth in CTR training. Specifically, (i) theoretically, we formulate the impact of fine-grained preferences on model stability as a Lipschitz constrain; (ii) empirically, we discover that scaling the supervision bandwidth can act as an implicit Lipschitz regularizer, stably optimizing existing CTR models to achieve better generalizability. Extensive experiments show that this scaled supervision significantly and consistently improves the optimization process and the performance of existing CTR models, even without the need for additional hyperparameter tuning.
Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research
Billions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external"' scientists---who may, in turn, resort to other means (e.g., rely on static datasets) for their studies. Given the abundance of literature on OSN, we - as academics - should take a step back and reflect on what we have done so far, after having written thousands of papers on OSN. This is the first paper that provides a holistic outlook to the entire body of research that focused on OSN - since the seminal work by Acquisti and Gross (2006). First, we search through over 1 million peer-reviewed publications, and derive 13,842 papers that focus on OSN: we organize the metadata of these works in the Minerva-OSN dataset, the first of its kind - which we publicly release. Next, by analyzing Minerva-OSN, we provide factual evidence elucidating trends and aspects that deserve to be brought to light - such as the predominant focus on Twitter or the difficulty in obtaining OSN data. Finally, as a constructive step to guide future research, we carry out an expert survey (n=50) with established scientists in this field, and coalesce suggestions to improve the status quo - such as an increased involvement of OSN owners. Our findings should inspire a reflection to "rescue" research on OSN. Doing so would improve the overall OSN ecosystem, benefiting both their owners and end-users - and, hence, our society.
SocialQuotes: Learning Contextual Roles of Social Media Quotes on the Web
Web authors frequently embed social media to support and enrich their content, creating the potential to derive webbased, cross-platform social media representations that can enable more effective social media retrieval systems and richer scientific analyses. As a step toward such capabilities, we introduce a novel language modeling framework that enables automatic annotation of roles that social media entities play in their embedded web context. Using related communication theory, we liken social media embeddings to quotes, formalize the page context as structured natural language signals, and identify a taxonomy of roles for quotes within the page context. We release SocialQuotes, a new data set built from the Common Crawl of over 32 million social quotes, 8.3k of them with crowdsourced quote annotations. Using SocialQuotes and the accompanying annotations, we provide a role classification case study, showing reasonable performance with modern-day LLMs, and exposing explainable aspects of our framework via page content ablations. We also classify a large batch of un-annotated quotes, revealing interesting cross-domain, cross-platform role distributions on the web.
Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI
Automated text annotation is a compelling use case for generative large language models (LLMs) in social media research. Recent work suggests that LLMs can achieve strong performance on annotation tasks; however, these studies evaluate LLMs on a small number of tasks and likely suffer from contamination due to a reliance on public benchmark datasets. Here, we test a human-centered framework for responsibly evaluating artificial intelligence tools used in automated annotation. We use GPT-4 to replicate 27 annotation tasks across 11 password-protected datasets from recently published computational social science articles in high-impact journals. For each task, we compare GPT-4 annotations against human-annotated ground-truth labels and against annotations from separate supervised classification models fine-tuned on human-generated labels. Although the quality of LLM labels is generally high, we find significant variation in LLM performance across tasks, even within datasets. Our findings underscore the importance of a human-centered workflow and careful evaluation standards: Automated annotations significantly diverge from human judgment in numerous scenarios, despite various optimization strategies such as prompt tuning. Grounding automated annotation in validation labels generated by humans is essential for responsible evaluation.
Join the Chat: How Curiosity Sparks Participation in Telegram Groups
This study delves into the mechanisms that spark user curiosity driving active engagement within public Telegram groups. By analyzing approximately 6 million messages from 29,196 users across 409 groups, we identify and quantify the key factors that stimulate users to actively participate (i.e., send messages) in group discussions. These factors include social influence, novelty, complexity, uncertainty, and conflict, all measured through metrics derived from message sequences and user participation over time. After clustering the messages, we apply explainability techniques to assign meaningful labels to the clusters. This approach uncovers macro categories representing distinct curiosity stimulation profiles, each characterized by a unique combination of various stimuli. Social influence from peers and influencers drives engagement for some users, while for others, rare media types or a diverse range of senders and media sparks curiosity. Analyzing patterns, we found that user curiosity stimuli are mostly stable, but, as the time between the initial message increases, curiosity occasionally shifts. A graph-based analysis of influence networks reveals that users motivated by direct social influence tend to occupy more peripheral positions, while those who are not stimulated by any specific factors are often more central, potentially acting as initiators and conversation catalysts. These findings contribute to understanding information dissemination and spread processes on social media networks, potentially contributing to more effective communication strategies.
Adopting Beliefs or Superficial Mimicry? Investigating Nuanced Ideological Manipulation of LLMs
Large Language Models (LLMs) have transformed natural language processing, but concerns have emerged about their susceptibility to ideological manipulation, particularly in politically sensitive areas. Previous research has largely focused on LLM biases through a binary Left vs. Right framework, often using explicit ideological prompts and fine-tuning with political question-answering datasets. In this work, we move beyond this binary approach to explore the extent to which LLMs can be influenced across a nuanced spectrum of political ideologies, from Progressive-Left to Conservative-Right. We introduce a novel multi-task dataset designed to reflect diverse ideological positions through tasks such as ideological question-answering, statement ranking, manifesto cloze completion, and Congress bill comprehension. By fine-tuning three LLMs—Phi-2, Mistral, and Llama-3—on this dataset, we evaluate their capacity to adopt and express these nuanced ideologies. Our findings indicate that fine-tuning significantly enhances nuanced ideological alignment, while explicit prompts provide only minor refinements. This highlights the models' susceptibility to subtle ideological manipulation, suggesting a need for more robust safeguards to mitigate these risks.
Extracting Participation in Collective Action from Social Media
Social media play a key role in mobilizing collective action, holding the potential for studying the pathways that lead individuals to actively engage in addressing global challenges. However, quantitative research in this area has been limited by the absence of granular and large-scale ground truth about the level of participation in collective action among individual social media users. To address this limitation, we present a novel suite of text classifiers designed to identify expressions of participation in collective action from social media posts, in a topic-agnostic fashion. Grounded in the theoretical framework of social movement mobilization, our classification captures participation and categorizes it into four levels: recognizing collective issues, engaging in calls-to-action, expressing intention of action, and reporting active involvement. We constructed a labeled training dataset of Reddit comments through crowdsourcing, which we used to train BERT classifiers and fine-tune Llama3 models. Our findings show that smaller language models can reliably detect expressions of participation (weighted F1=0.71), and rival larger models in capturing nuanced levels of participation. By applying our methodology to Reddit, we illustrate its effectiveness as a robust tool for characterizing online communities in innovative ways compared to topic modeling, stance detection, and keyword-based methods. Our framework contributes to Computational Social Science research by providing a new source of reliable annotations useful for investigating the social dynamics of collective action.
Changes in YouTube's Content Moderation Policy Had Little Detectable Impact on Election Denial Content
While a growing body of research examines the effect of deplatforming, the blanket banning of certain types of content and users, on social media discourse, comparatively little research examines the effect of replatforming, when social media companies remove such blanket bans. We exploit such a policy change, YouTube’s June 2nd decision to stop removing content denying the validity of the 2020 US Presidential Election, to examine the effect of replatforming content. Using data from YouTube, Facebook, Telegram conspiracy groups, and Google, we find no evidence that YouTube's policy change increased demand for or supply of election denial content over the short term. These results are consistent across all platforms and three different methodologies: regression discontinuity models, structural topic models, and a Bayesian structural time series model. This suggests that replatforming alone, when conducted after the effective marginalization of the targeted content, has minimal short-term effects on the spread of previously-prohibited content
Do Bots Do It Better? Analyzing the Effectiveness of Automated Agents in State-Sponsored Information Operations
State sponsored information operations, or SSIOs, are a growing problem across many of the information spaces we inhabit online. These instances of coordinated misinformation and propaganda have been perpetrated by over 80 state actors in the last decade, and have been used to exert influence on digital media consumption habits, discussions of contentious issues, and even national elections. Concern over the power that SSIOs wield is only growing as the proliferation of automated tools and services is making it easier than ever to launch large-scale manipulation campaigns. But what role do such automated agents play within the broader operations that they are deployed in? Are they even successful at making an impact in information spaces online? In this work, we address both of these questions through the use of a sequence-based clustering method and advanced linear modeling. Using these methods, we investigate the relationship between agent automation, role, and network characteristics and how much success those agent's achieve over the course of their lifetimes. We find that automated agents perform worse across every success metric compared to human agents, and that they play a smaller, supporting role to the primarily human SSIO workforce. What's more, we find that the extent to which agent's engage in amplifying- or producing-centric roles is by far the biggest determinant of how successful they will be, highlighting the importance of social-roles in the analysis of automated agents.
Coordinated Reply Attacks in Influence Operations: Characterization and Detection
Coordinated reply attacks are a tactic observed in online influence operations and other coordinated campaigns to support or harass targeted individuals, or influence them or their followers. Despite its potential to influence the public, past studies have yet to analyze or provide a methodology to detect this tactic. In this study, we characterize coordinated reply attacks in the context of influence operations on Twitter. Our analysis reveals that the primary targets of these attacks are influential people such as journalists, news media, state officials, and politicians. We propose two supervised machine-learning models, one to classify tweets to determine whether they are targeted by a reply attack, and one to classify accounts that reply to a targeted tweet to determine whether they are part of a coordinated attack. The classifiers achieve AUC scores of 0.88 and 0.97, respectively. These results indicate that accounts involved in reply attacks can be detected, and the targeted accounts themselves can serve as sensors for influence operation detection.
How Influencers and Multipliers Drive Polarization and Issue Alignment on Twitter/X
We investigate the polarization of the German Twittersphere by extracting the main issues discussed and the signaled opinions of users towards those issues based on (re)tweets concerning trending topics. The dataset covers daily trending topics from March 2021 to July 2023. At the opinion level, we show that the online public sphere is largely divided into two camps, one consisting mainly of left-leaning, and another of right-leaning accounts. Further we observe that political issues are strongly aligned, contrary to what one may expect from surveys. This alignment is driven by two cores of strongly active users: influencers, who generate ideologically charged content, and multipliers, who facilitate the spread of this content. The latter are specific to social media and play a crucial role as intermediaries on the platform by curating and amplifying very specific types of content that match their ideological position, resulting in the overall observation of a strongly polarized public sphere. These results contribute to a better understanding of the mechanisms that shape online public opinion, and have implications for the regulation of platforms.
Perception of Visual Content: Differences Between Humans and Foundation Models
Human-annotated content is often used to train machine learning (ML) models. However, recently, language and multi-modal foundational models have been used to replace and scale-up human annotator's efforts. This study explores the similarity between human-generated and ML-generated annotations of images across diverse socio-economic contexts (RQ1) and their impact on ML model performance and bias (RQ2). We aim to understand differences in perception and identify potential biases in content interpretation. Our dataset comprises images of people from various geographical regions and income levels, covering various daily activities and home environments. ML captions and human labels show highest similarity at a low-level, i.e., types of words that appear and sentence structures, but all annotations are consistent in how they perceive images across regions. ML Captions resulted in best overall region classification performance, while ML Objects and ML Captions performed best overall for income regression. ML annotations worked best for action categories, while human input was more effective for non-action categories. These findings highlight the notion that both human and machine annotations are important, and that human-generated annotations are yet to be replaceable.
Practical Guidelines for Ideology Detection Pipelines and Psychosocial Applications
Online ideology detection is crucial for downstream tasks, like countering ideologically motivated violent extremism and modeling opinion dynamics. However, two significant issues arise in practitioners' deployment. Firstly, gold-standard training data is prohibitively labor-intensive to collect and has limited reusability beyond its collection context (i.e., time, location, and platform). Secondly, to circumvent expense, researchers employ ideological signals (such as hashtags shared). Unfortunately, these signals' annotation requirements and context transferability are largely unknown, and the bias they induce remains unquantified. This study provides guidelines for practitioners requiring real-time detection of left, right, and extreme ideologies in large-scale online settings. We propose a framework for pipeline constructions, describing ideology signals by their associated labor and context transferability. We evaluate many constructions, quantifying the bias associated with signals and describing a pipeline that outperforms state-of-the-art methods (0.95 AUC ROC). We showcase the capabilities of our pipeline on five datasets containing more than 1.12 million users. We set out to investigate whether the findings in the psychosocial literature, developed for the offline environment, apply to the online setting. We evaluate at scale several psychosocial hypotheses that delineate ideologies concerning morality, grievance, nationalism, and dichotomous thinking. We find that right-wing ideologies use more vice-moral language, have more grievance-filled language, exhibit increased black-and-white thinking patterns, and have a greater association with national flags. This research empowers practitioners with guidelines for ideology detection, and case studies for its application, fostering a safer and better understood digital landscape.
Polarized Online Discourse on Abortion: Frames and Hostile Expressions Among Liberals and Conservatives
Abortion has been one of the most divisive issues in the United States. Yet, missing is comprehensive longitudinal evidence on how political divides on abortion are reflected in public discourse over time, on a national scale, and in response to key events before and after the overturn of Roe v Wade. We analyze a corpus of over 3.5M tweets related to abortion over the span of one year (January 2022 to January 2023) from over 1.1M users. We estimate users' ideology and rely on state-of-the-art transformer-based classifiers to identify expressions of hostility and extract five prominent frames surrounding abortion. We use those data to examine (a) how prevalent were expressions of hostility (i.e., anger, toxic speech, insults, obscenities, and hate speech), (b) what frames liberals and conservatives used to articulate their positions on abortion, and (c) the prevalence of hostile expressions in liberals and conservative discussions of these frames. We show that liberals and conservatives largely mirrored each other's use of hostile expressions: as liberals used more hostile rhetoric, so did conservatives, especially in response to key events. In addition, the two groups used distinct frames and discussed them in vastly distinct contexts, suggesting that liberals and conservatives have differing perspectives on abortion. Lastly, frames favored by one side provoked hostile reactions from the other: liberals use more hostile expressions when addressing religion, fetal personhood, and exceptions to abortion bans, whereas conservatives use more hostile language when addressing bodily autonomy and women's health. This signals disrespect and derogation, which may further preclude understanding and exacerbate polarization.
Exploring the Limits of Zero Shot Vision Language Models for Hate Meme Detection: The Vulnerabilities and their Interpretations
There is a rapid increase in the use of multimedia content in current social media platforms. One of the highly popular forms of such multimedia content are memes. While memes have been primarily invented to promote funny and buoyant discussions, malevolent users exploit memes to target individuals or vulnerable communities, making it imperative to identify and address such instances of hateful memes. Thus social media platforms are in dire need for active moderation of such harmful content. While manual moderation is extremely difficult due to the scale of such content, automatic moderation is challenged by the need of good quality annotated data to train hate meme detection algorithms. This makes a perfect pretext for exploring the power of modern day vision language models (VLMs) that have exhibited outstanding performance across various tasks. In this paper we study the effectiveness of VLMs in handling intricate tasks such as hate meme detection in a completely zero-shot setting so that there is no dependency on annotated data for the task. We perform thorough prompt engineering and query state-of-the-art VLMs using various prompt types to detect hateful/harmful memes. We further interpret the misclassification cases using a novel superpixel based occlusion method. Finally we show that these misclassifications can be neatly arranged into a typology of error classes the knowledge of which should enable the design of better safety guardrails in future. Code and other relevant sources are available online. Warning: Contains potentially offensive content.
Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities
In the wake of the 2024 US presidential election, pundits on both the left and the right pointed to a conservative backlash against "woke politics" to explain the election's outcome. These politics, rooted in substantive beliefs about equity and justice--and particularly racial justice--owe their most recent rise to prominence to the Black Lives Matter (BLM) movement. A significant body of work, both qualitative and quantitative, has documented how BLM was able to move these beliefs from the margin to the mainstream. In this paper, we focus on the words that index these beliefs, devising a novel method of modeling semantic leadership across a set of communities associated with the BLM movement that is informed by domain-specific theory about Black Twitter. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find evidence at scale of the leadership role of BLM activists and progressives, as well as of Black celebrities. We also find evidence of sustained conservative engagement with this discourse, suggesting an alternative explanation for how we have arrived at the present political moment.
Protection from Evil and Good: The Differential Effects of Page Protection on Wikipedia Article Quality
Wikipedia, the Web's largest encyclopedia, frequently faces content disputes or malicious users seeking to subvert its integrity. Administrators can mitigate such disruptions by enforcing "page protection" that selectively limits contributions to specific articles to help prevent the degradation of content. However, this practice contradicts one of Wikipedia's fundamental principles—that it is open to all contributors—and may hinder further improvement of the encyclopedia. In this paper, we examine the effect of page protection on article quality to better understand whether and when page protections are warranted. Using decade-long data on page protections from the English Wikipedia, we conduct a quasi-experimental study analyzing pages that received "requests for page protection"—written appeals submitted by Wikipedia editors to administrators to impose page protections. We match pages that indeed received page protection with similar pages that did not and quantify the causal effect of the interventions on a well-established measure of article quality. Our findings indicate that the effect of page protection on article quality depends on the characteristics of the page prior to the intervention: high-quality articles are affected positively, as opposed to low-quality articles that are impacted negatively. Subsequent analysis suggests that high-quality articles degrade when left unprotected, whereas low-quality articles improve. Overall, with our study, we outline page protections on Wikipedia and inform best practices on whether and when to protect an article.
Does Content Moderation Lead Users Away from Fringe Movements? Evidence from a Recovery Community
Online platforms have sanctioned individuals and communities associated with ‘fringe’ movements linked to hate speech, violence, and terrorism — but can these sanctions contribute to the abandonment of these movements? Here, we investigate this question through the lens of r/exredpill, a recovery community on Reddit meant to help individuals leave movements within the Manosphere, a conglomerate of fringe Web-based movements focused on men’s issues. We conduct an observational study on the impact of sanctioning some of Reddit’s largest Manosphere communities on the activity levels and user influx of r/exredpill, the largest associated recovery subreddit. We find that banning a related radical community positively affects participation in r/exredpill in the period following the ban. Yet, quarantining the community, a softer moderation intervention, yields no such effects. We show that the effect induced by banning a radical community is stronger than for some of the widely discussed real-world events related to the Manosphere and that moderation actions against the Manosphere do not cause a spike in toxicity or malicious activity in r/exredpill. Overall, our findings suggest that content moderation acts as a deradicalization catalyst.
Scalable Reputation Management: A Multi-Task Prompting Approach Using Fine-Tuned PLMs for Sentiment and Topic Classification
Social media platforms provide a direct, real-time channel for companies to engage with their audiences, making the management of corporate reputation on social media a pivotal factor for organizational success. Reputation is shaped by factors such as the relationship with the audience, crisis communication, public sentiment, and the topics most frequently associated with the company. Although reputation is often viewed as an intangible asset, it can be quantified and monitored through various metrics. Advances in AI and Pretrained Language Models (PLMs) have automated tasks such as sentiment analysis, sentiment strength assessment, and topic classification, creating new opportunities to manage reputation more efficiently. However, current PLM solutions typically address these tasks individually and require specialized training for each company, limiting scalability and flexibility. To address these challenges, we propose a novel system framework to assist public relations firms by leveraging PLMs for automatic classification. We evaluated this approach through experiments on four companies, comparing zero-shot and fine-tuned models in task-specific and multi-task configurations. Additionally, we explored the transfer of knowledge between client companies using fine-tuned models. Results indicate that multi-task, multi-company fine-tuned PLM models offer simpler system management with competitive performance compared to highly specialized models.
Mental Health Impact of the COVID-19 Pandemic on College Students: A Quasi-Experimental Study on Social Media
Given the limited understanding of how the COVID-19 pandemic impacted mental health on college campuses, this paper examines the evolution of the mental health of college students since the onset of the pandemic. We conducted a large-scale study on over 1.2M posts on 173 U.S. college subreddits over 17 months. In particular, we adopted a quasi-experimental approach to examine how the different stages of the pandemic (isolation period, normalization period, and vaccination period) impacted changes in social media discussions of college students. We measured the temporal shifts in the symptomatic mental health expressions and topics of discussion on college subreddits. We find that while the expressions of depression, anxiety, stress, and suicidal ideation significantly increased in the isolation period. Interestingly, these expressions gradually subsided in the normalization period, only to resurface in the vaccination period. We also find unique occurrences of discussion across social, academic, health, and COVID-19-induced topics. Our findings reveal that despite the fragility of college students' mental health in the face of crisis, college students show resilience with sufficient time. We discuss the implications of our work in terms of building tools for real-time comprehension of college students' mental health, and in designing timely and tailored mental health support for college students.
Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs
Social media platforms allow users to create polls to gather public opinion on diverse topics. However, we know little about what such polls are used for and how reliable they are, especially in significant contexts like elections. Focusing on the 2020 presidential elections in the U.S., this study shows that outcomes of election polls on Twitter deviate from election results despite their prevalence. Leveraging demographic inference and statistical analysis, we find that Twitter polls are disproportionately authored by male Republicans and exhibit a large bias towards candidate Donald Trump in comparison to mainstream polls. We investigate potential sources of biased outcomes from the point of view of inauthentic, automated, and counter-normative behavior. Using social media experiments and interviews with poll authors, we identify inconsistencies between public vote counts and those privately visible to poll authors, with the gap potentially attributable to purchased votes. We find that election polls tend to be more biased, contain more questionable votes, and attract more bots before the election day than after. We highlight and compare key factors contributing to biased poll outcomes. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple of thousand such polls were posted in 2020. The study discusses the implications of biased election polls in the context of transparency and accountability of social media platforms.
“I Don’t Think TikTok Really Cares About the Truth:” Experiences of Users Who Are Low Vision or Blind with Misinformation on TikTok
Moderating misinformation on social media is a complex task of warning users about potentially harmful content while remaining reliable, unbiased, and non-judgmental. Though this is a valid concern, it doesn't exempt platforms like TikTok from making their soft moderation interventions inaccessible for users who are low vision or blind. Through interviews with 13 low vision or blind TikTok users, we learned that this was exactly the case - the informative cues used for soft moderation were inaccessible in 93% of the cases. To address this participatory exclusion, our participants proposed redesigns for navigable informative cues through auditory means or "audio frictions" that both warn the users and provide them with contextual information on why a particular content might be misleading, false, or generally harmful.
Auditing Yelp’s Business Ranking and Review Recommendation Through the Lens of Fairness
Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transparency in the model’s internal workings. In many web applications, such as Yelp, it is challenging, if not impossible, to manipulate their inputs systematically to identify bias in the output. Yelp connects users and businesses, where users identify new businesses and simultaneously express their experiences through reviews. Yelp recommendation software moderates user-provided content by categorizing it into recommended and not-recommended sections. The recommended reviews, among other attributes, are used by Yelp’s ranking algorithm to rank businesses in a neighborhood. Due to Yelp’s substantial popularity and its high impact on local businesses’ success, understanding the bias of its algorithms is crucial. This data-driven study, for the first time, investigates the bias of Yelp’s business ranking and review recommendation system. We examine three hypotheses to assess if Yelp’s recommendation software shows bias against reviews of less established users with fewer friends and reviews and if Yelp’s business ranking algorithm shows bias against restaurants located in specific neighborhoods, particularly in hotspot regions, with specific demographic compositions. Our findings show that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants’ location in hotspot regions and their average exposure. Furthermore, we observed some cases of severe disparity bias in cities where the hotspots are in neighborhoods with less demographic diversity or higher affluence and education levels.
Seyyed Mohammad Sadegh Moosavi Khorzooghi
GNN and LLM Insights: Multimodal Cues and Gender Disparities in Video Conversations
As video content on online platforms continues to increase, understanding the complex aspects of interpersonal communication becomes crucial. Central to this exploration is the pressing issue of gender bias, which manifests in multimodal interactions through visual, vocal, or verbal cues. These interactions present challenges in extracting and interpreting the subtle cues that may point to underlying biases. To tackle these challenges, we introduce a semi-automatic extraction of features and knowledge from user-generated content on video web platforms. Using 1,091 unstructured multi-participant video conversations from Shark Tank, we examine whether the multimodal cues (e.g., emotions) of a conversational participant (e.g., entrepreneur) affect another participant (e.g., investor) differently due to gender biases. Our methodology employs advanced deep learning algorithms for cues extraction and leverages Graph Neural Networks to model the multi-participant conversations. To complement our findings, we utilize textual features extracted through our methodology and employ GPT-4 to simulate decision-making scenarios, thereby assessing its analytical capabilities and potential gender biases.
More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram
To address the increasing prevalence of (audio)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approaches for analyzing multimodal online content. However, existing research often neglects visual content beyond memes, and in addition lacks methods to compare topic models across modalities. Our study addresses these gaps by applying multimodal topic modeling for analyzing conspiracy theories in German-language Telegram channels. We use BERTopic with CLIP for the analysis of textual and visual data in a corpus of ∼40,000 Telegram messages posted in October 2023 in 571 German-language Telegram channels known for disseminating conspiracy theories. Through this data set, we provide insights into unimodal and multimodal topic models by analyzing the symmetry and intersections of topics across modalities. We demonstrate the variety of textual and visual content shared in the channels discovered through the topic modeling, and propose a conceptual framework for the analysis of textual and visual discursive strategies in the communication of conspiracy theories. We apply the framework in a case study of the topic group Israel Gaza.
Going /k/ommando: Gun Culture in Fringe Online Communities
The increasing frequency of mass shootings in the United States has become alarmingly common, prompting discussions about gun control. While gun control in the US involves complex legal issues, cultural factors---particularly ``gun culture''---play a significant but often overlooked role. Although the role of social media in shaping culture is well-documented, the intersection of gun culture and fringe online communities, like 4chan, remains unclear. This gap is particularly concerning given the rise in mass shootings and the online radicalization of some shooters. To address this gap, we explore gun culture on /k/, 4chan's weapons board. More specifically, we employ various NLP techniques to analyze over 4M posts on /k/ and contextualize the discussion within the broader body of theoretical framework of gun culture. Our findings reveal that discussions on /k/ cover a wide array of topics, with a significant focus on law-related discussions---over 17% of gun-related conversations on /k/ revolve around legal matters. Additionally, our analysis uncovers the presence of extreme viewpoints surrounding firearms, often manifesting as gun fetishism. These insights can be valuable for a range of stakeholders including social media platform, in efforts to address content moderation and de-radicalization
Avatar Communication Provides More Efficient Online Social Support Than Text Communication
Online communication via avatars provides a richer online social experience than text communication. This reinforces the importance of online social support. Online social support is effective for people who lack social resources because of the anonymity of online communities. We aimed to understand online social support via avatars and their social relationships to provide better social support to avatar users. Therefore, we administered a questionnaire to three avatar communication service users (Second Life, ZEPETO, and Pigg Party) and three text communication service users (Facebook, X, and Instagram) (N=8,947). There was no duplication of users for each service. By comparing avatar and text communication users, we examined the amount of online social support, stability of online relationships, and the relationships between online social support and offline social resources (e.g., offline social support). We observed that avatar communication service users received more online social support, had more stable relationships, and had fewer offline social resources than text communication service users. However, the positive association between online and offline social support for avatar communication users was more substantial than for text communication users. These findings highlight the significance of realistic online communication experiences through avatars, including nonverbal and real-time interactions with co-presence. The findings also highlighted avatar communication service users' problems in the physical world, such as the lack of offline social resources. This study suggests that enhancing online social support through avatars can address these issues. This could help resolve social resource problems, both online and offline in future metaverse societies.
Niche Dynamics in Complex Online Community Ecosystems
Online communities are important organizational forms where members socialize and share information. Curiously, different online communities often overlap considerably in topic and membership. Recent research has investigated competition and mutualism among overlapping online communities through the lens of organizational ecology; however, it has not accounted for how the nonlinear dynamics of online attention may lead to episodic competition and mutualism. Neither has it explored the origins of competition and mutualism in the processes by which online communities select or adapt to their niches. This paper presents a large-scale study of 8,806 Reddit communities belonging to 1,919 clusters of high user overlap over a 5-year period. The method uses nonlinear time series methods to infer bursty, often short-lived ecological dynamics. Results reveal that mutualism episodes are longer lived and slightly more frequent than competition episodes. Next, it tests whether online communities find their niches by specializing to avoid competition using panel regression models. It finds that competitive ecological interactions lead to decreasing topic and user overlaps; however, changes that decrease such niche overlaps do not lead to mutualism. The discussion considers future designs for online community ecosystem management.
Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models
Masculine defaults are widely recognized as a significant type of gender bias, but they are often unseen as they are under-researched. Masculine defaults involve three key parts: (i) the cultural context, (ii) the masculine characteristics or behaviors, and (iii) the reward for, or simply acceptance of, those masculine characteristics or behaviors. In this work, we study discourse-based masculine defaults, and propose a twofold framework for (i) the large-scale discovery and analysis of gendered discourse words in spoken content via our Gendered Discourse Correlation Framework (GDCF); and (ii) the measurement of the gender bias associated with these gendered discourse words in LLMs via our Discourse Word-Embedding Association Test (D-WEAT). We focus our study on podcasts, a popular and growing form of social media, analyzing 15,117 podcast episodes. We analyze correlations between gender and discourse words -- discovered via LDA and BERTopic -- to automatically form gendered discourse word lists. We then study the prevalence of these gendered discourse words in domain-specific contexts, and find that gendered discourse-based masculine defaults exist in the domains of business, technology/politics, and video games. Next, we study the representation of these gendered discourse words from a state-of-the-art LLM embedding model from OpenAI, and find that the masculine discourse words have a more stable and robust representation than the feminine discourse words, which may result in better system performance on downstream tasks for men. Hence, men are rewarded for their discourse patterns with better system performance by one of the state-of-the-art language models -- and this embedding disparity is a representational harm and a masculine default.
A Multimodal Prompt-based Framework for Analyzing Code-Mixed and Low-Resource Memes
The emergence of social media has led memes to become a powerful mode of communication, blending text, images, and emojis. However, this surge in meme usage has also seen a rise in offensive material. With manual content moderation proving impractical due to the sheer volume of data, there's a pressing need for automated methods to identify harmful memes. Yet, existing research predominantly targets high-resource languages such as English, neglecting low-resource ones like Nepali. To bridge this gap, we introduce the first Nepali meme dataset annotated for hate speech and sentiment. Our contributions are threefold: (1) We create and release NeMeme, a unique dataset featuring Nepali and code-mixed Nepali memes (combining Nepali and English). (2) We evaluate NeMeme using cutting-edge unimodal and multimodal models to establish initial performance benchmarks. (3) We introduce MemeNePAL, a novel multimodal framework employing prompt-assisted learning to effectively categorize Nepali memes. MemeNePAL overcomes the shortcomings of prior state-of-the-art (SOTA) techniques, which were designed for high-resource languages and struggle with Nepali's linguistic differences and cultural subtleties. This work not only promotes inclusivity in content moderation research but also aligns with UN Sustainable Development Goals such as promoting well-being, reducing inequalities, and fostering peace. We adhere to FAIR principles by making the dataset publicly available.
Characterizing Knowledge Manipulation in a Russian Wikipedia Fork
Wikipedia is powered by MediaWiki, a free and open-source software that is also the infrastructure for many other wiki-based online encyclopedias. These include the recently launched website Ruwiki, which has copied and modified the original Russian Wikipedia content to conform to Russian law. To identify practices and narratives that could be associated with different forms of knowledge manipulation, this article presents an in-depth analysis of this Russian Wikipedia fork. We propose a methodology to characterize the main changes with respect to the original version. The foundation of this study is a comprehensive comparative analysis of more than 1.9M articles from Russian Wikipedia and its fork. Using meta-information and geographical, temporal, categorical, and textual features, we explore the changes made by Ruwiki editors. Furthermore, we present a classification of the main topics of knowledge manipulation in this fork, including a numerical estimation of their scope. This research not only sheds light on significant changes within Ruwiki, but also provides a methodology that could be applied to analyze other Wikipedia forks and similar collaborative projects.
Exploring Unknown Social Networks for Discovering Hidden Nodes
In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in machine learning. Leveraging the structure of the subgraph gradually obtained from graph exploration, we construct prediction models to identify target hidden nodes in unknown social graphs. Through empirical investigations of real social graphs, we investigate the efficiency of graph exploration strategies in uncovering hidden nodes. Our results show that our graph exploration strategies discover hidden nodes with an efficiency comparable to that when the graph structure is known. Specifically, the query cost of discovering 10% of the hidden nodes is at most only 1.2 times that when the topology is known, and the query-cost multiplier for discovering 90% of the hidden nodes is at most only 1.4. Furthermore, our results suggest that using node embeddings, which are low-dimensional vector representations of nodes, for hidden-node discovery is a double-edged sword: it is effective in certain scenarios but sometimes degrades the efficiency of node discovery. Guided by this observation, we examine the effectiveness of using a bandit algorithm to combine the prediction models that use node embeddings with those that do not, and our analysis shows that the bandit-based graph exploration strategy achieves efficient node discovery across a wide array of settings.
Experiences of Censorship on TikTok Across Marginalised Identities
TikTok has seen exponential growth as a platform, fuelled by the success of its proprietary recommender algorithm which serves tailored content to every user - though not without controversy. Users complain of their content being unfairly suppressed by "the algorithm", particularly users with marginalised identities such as LGBTQ+ users. Together with content removal, this suppression acts to censor what is shared on the platform. Journalists have revealed biases in automatic censorship, as well as human moderation. We investigate experiences of censorship on TikTok, across users marginalised by their gender, LGBTQ+ identity, disability or ethnicity. We survey 627 UK-based TikTok users and find that marginalised users often feel they are subject to censorship for content that does not violate community guidelines. We highlight many avenues for future research into censorship on TikTok, with a focus on users' folk theories, which greatly shape their experiences of the platform.
“Reasoning” with Rhetoric: On the Style-Evidence Tradeoff in LLM-Generated Counter-Arguments
Large language models (LLMs) play a key role in generating evidence-based and stylistic counter-arguments, yet their effectiveness in real-world applications has been underexplored. Previous research often neglects the balance between evidentiality and style, which are crucial for persuasive arguments. To address this, we evaluated the effectiveness of stylized evidence-based counter-argument generation in Counterfire, a new dataset of 38,000 counter-arguments generated by revising counter-arguments to Reddit’s ChangeMyView community to follow different discursive styles. We evaluated generic and stylized counter-arguments from basic and fine-tuned models such as GPT-3.5, PaLM-2, and Koala-13B, as well as newer models (GPT-4o, Claude Haiku, LLaMA-3.1) focusing on rhetorical quality and persuasiveness. Our findings reveals that humans prefer stylized counter-arguments over the original outputs, with GPT-3.5 Turbo performing well, though still not reaching human standards of rhetorical quality nor persuasiveness. Additionally, our work created a novel argument triplets dataset for studying style control, with human preference labels that provide insights into the tradeoffs between evidence integration and argument quality.
Reddit Rehab: User Migration in Response to Mobile Client Shutdowns
This paper investigates the behavior of Reddit users who relied on alternative mobile apps, such as Apollo and RiF, before and after their forced shutdown by Reddit on July 1, 2023. The announcement of the shutdown led many observers to predict significant negative consequences, such as mass migration away from the platform. Using data from January to November 2023, we analyze user engagement and migration rates for users of these alternative clients before and after the forced discontinuation of their apps. We find that 22% of alternative client users permanently left Reddit as a result, and 45% of the users who openly threatened to leave if the changes were enacted followed through with their threats. Overall, we find that the shutdown of third-party apps had no discernible impact on overall platform activity. While the preceding protests were severe, ultimately for most users the cost of switching to the official client was likely far less than the effort required to switch to an entirely different platform. Scientific attention has increased to understand the contributing factors and effects of migration between online platforms, but real-world examples with available data remain rare. Our study addresses this by examining a large-scale online migratory movement.
Can't Stop Scrolling: Understanding the Online Behavioral Factors and Trends of Short-Video Addiction
The pervasive use of short-video applications has raised concerns about their potential negative effects on users, particularly addiction. Existing research often relies on psychological questionnaires, which lack real-world behavioral data, limiting scalability and analytical depth. To address this, we assess the addiction status of short-video platform users using a standardized psychometric questionnaire, combined with platform behavioral data and interview responses to uncover features associated with addiction. Using feature-based modeling, we scale to a dataset of 10,111 addiction-labeled users and identify key indicators of addiction, including prolonged daily watch time, especially at night, and excessive video consumption, while also revealing that higher watch frequency is not fully correlated with addiction. Additionally, we find that addicted users tend to consume a narrower range of content, suggesting a filter bubble effect. Our large-scale analysis provides valuable insights for platform designers, policymakers, and mental health professionals seeking to promote healthier engagement and mitigate the risks of short-video addiction.
Leveraging Large Language Models for Review Classification and Rating Estimation of Mental Health Applications
Large Language Models (LLMs) can analyze large datasets semantically. However, research on applying LLMs for mental health text classification is relatively new and developing. Existing methods often use supervised, deep, and reinforcement learning, which rely heavily on fine-tuning and reward models. To investigate whether LLMs can assist in recommending mental health apps based on user reviews, our study collected approximately 200k user reviews from 73 mental health mobile applications. We instructed selected LLMs to classify individual reviews into 1-5 star ratings, subsequently averaging these results to derive an overall rating for each app reflecting current user feedback. While the best supervised learning method in our experiments achieved an F1-Score of 0.79 which required significantly more human effort, the GPT-4 and Gemini 1.5 Pro delivered a strong ‘out-of-the-box’ performance with an overall F1-Score of 0.76. We provide further statistical comparisons and discussions of the performance of these models for the text classification task. Using a crowdsourcing platform to determine agreement levels, we observed that human ratings align closely with GPT ratings. In addition, we analyze specific features and concerns highlighted in mental health app reviews. Alongside our analysis, we make our data available for further experimentation and benchmarking.
Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains
Proactive content moderation requires platforms to rapidly and continuously evaluate the credibility of websites. Leveraging the direct and indirect paths users follow to unreliable websites, we develop a website credibility classification and discovery system that integrates both webgraph and large-scale social media contexts. We additionally introduce the concept of dredge words—terms or phrases for which unreliable domains rank highly on search engines—and provide the first exploration of their usage on social media. Our graph neural networks that combine webgraph and social media contexts generate to state-of-the-art results in website credibility classification and significantly improves the top-k identification of unreliable domains. Additionally, we release a novel dataset of dredge words, highlighting their strong connections to both social media and online commerce platforms.
Integrated or Segregated? User Behavior Change After Cross-Party Interactions on Reddit
It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring out-party community engagement, which is likely part of a more general dynamic of feedback-boosted activity.
Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach
Influence Maximization (IM) in temporal graphs focuses on identifying influential ``seeds'' that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue.
StyleLink: User Identity Linkage Across Social Media with Stylometric Representations
User identity linkage (UIL) is the task of aligning user identities of the same user across different social network platforms. Although existing approaches have explored various aspects such as different user profile attributes and social network structures, the writing styles from user-generated texts, which is commonly known as stylometry, remain relatively underexplored. In this paper, we propose a novel Graph Neural Network (GNN)-based model named StyleLink, which leverages both social network structures and stylometric features derived from user-generated texts to address the UIL problem in an integrated manner. Our model utilizes GNNs to incorporate both stylometric features and the network structure for each social network, effectively embedding the network and enhancing user representation. This is the first work to incorporate stylometric features into GNNs to embed social networks and then conduct UIL between two embedding spaces. Extensive experiments on real-world social network datasets demonstrate the superior performance of StyleLink over existing state-of-the-art methods, achieving higher accuracy in user linkage and improved ranking of identity matches. In addition, we explore the effects of different linguistic characteristics in the identification of user identities and visualizes the effects of applying GNNs for better social network embedding.
What Do You Learn About Hard News from Soft News Outlets - A Case Study from People Magazine Online
People are increasingly avoiding the news in a phenomenon termed news avoidance. Prior work has found that many people are not following political news, with the median American consuming zero articles per year from traditional news outlets. This behavior has troubling implications, as a well-informed public is typically considered essential to a functioning democracy. However, it is possible that people are accidentally picking up political information through entertainment or "soft news" outlets. The subject of this paper is People Magazine, a soft news outlet selected for its popularity (with an average of 187.1 million monthly visits). To understand how political news is covered by entertainment and soft news outlets, we make the following contributions: (1) we propose two potential and complementary frameworks for differentiating hard news content from soft news content; (2) we collect a large dataset of articles published on People Magazine's website; (3) we apply our frameworks to this dataset and contribute an analysis of the political content offered by a soft news outlet; (4) we evaluate our framework on two additional platforms. Together, these contributions additively result in the first exploration of the political content of individual news stories - all prior work has labeled entire outlets as hard or soft. As people increasingly avoid outlets with hard news, our work shows that they may still encounter political information through soft news outlets, a finding that has critical implications for political communication.
Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok
Opaque algorithms disseminate and mediate the content that users consume on online social media platforms. This algorithmic mediation serves users with contents of their liking, on the other hand, it may cause several inadvertent risks to society at scale. While some of these risks, e.g., filter bubbles or dissemination of hateful content, are well studied in the community, behavioral addiction, designated by the Digital Services Act (DSA) as a potential systemic risk, has been understudied. In this work, we aim to study if one can effectively diagnose behavioral addiction using digital data traces from social media platforms. Focusing on the TikTok short-format video platform as a case study, we employ a novel mixed methodology of combining survey responses with data donations of behavioral traces. We survey 1590 TikTok users and stratify them into three addiction groups (i.e., less/moderately/highly likely addicted). Then, we obtain data donations from 107 surveyed participants. By analyzing users' data we find that, among others, highly likely addicted users spend more time watching TikTok videos and keep coming back to TikTok throughout the day, indicating a compulsion to use the platform. Finally, by using basic user engagement features, we train classifier models to identify highly likely addicted users with F1 >= 0.55. The performance of the classifier models suggests predicting addictive users solely based on their usage is rather difficult.
PHAnToM: Persona-Based Prompting Has an Effect on Theory-of-Mind Reasoning in Large Language Models
The use of LLMs in natural language reasoning has shown mixed results, sometimes rivaling or even surpassing human performance in simpler classification tasks while struggling with social-cognitive reasoning, a domain where humans naturally excel. These differences have been attributed to many factors, such as variations in prompting and the specific LLMs used. However, no reasons appear conclusive, and no clear mechanisms have been established in prior work. In this study, we empirically evaluate how role-playing persona-based prompting influences Theory-of-Mind (ToM) reasoning capabilities. Grounding our research in psychological theory, we found that, beyond the inherent variance in the complexity of reasoning tasks, ToM performance differences arise because of socially-motivated prompting differences. In an era where prompt engineering with role-play is a typical approach to adapt LLMs to new contexts, our research advocates caution as models that adopt specific personas might potentially result in errors in social-cognitive reasoning.
ID-XCB: Data-Independent Debiasing for Fair and Accurate Transformer-Based Cyberbullying Detection
The use of swear words is a common proxy to collect datasets with cyberbullying incidents, which increases the chances of collecting such events that are otherwise hard to find. However, datasets collected through this means also have a risk of introducing biases in cyberbullying detection models which can learn spurious associations between swear words and the presence of incidents. In this study, we undertake a pioneering study of measuring and mitigating swearing bias in cyberbullying detection tasks. Initially, we employ word-level bias measures to demonstrate the distinctive features related to swearing biases in transformer-based cyberbullying detection models. Subsequently, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and a debias fine-tuning approach aimed at alleviating model attention to bias-inducing words without impacting overall model performance. Lastly, we explore ID-XCB on two popular session-based cyberbullying detection datasets along with a comprehensive set of ablation studies and model generalisation studies. Our findings show that ID-XCB learns robust cyberbullying detection capabilities while mitigating biases tied to swear word usage. It consistently outperforms state-of-the-art debiasing methods in terms of both performance improvement and bias mitigation. In addition, by combining quantitative and qualitative analyses, we demonstrate the potential for generalisability of our approach when tackling unseen data.
Detecting Harassment and Defamation in Cyberbullying with Emotion-Adaptive Training
Existing research on detecting cyberbullying incidents on social media has primarily concentrated on harassment and is typically approached as a binary classification task. However, cyberbullying encompasses various forms, such as denigration and harassment, which celebrities frequently face. Furthermore, suitable training data for these diverse forms of cyberbullying remains scarce. In this study, we first develop a celebrity cyberbullying dataset that encompasses two distinct types of incidents: harassment and defamation. We investigate various types of transformer-based models, namely masked (RoBERTa, Bert and DistilBert), replacing (Electra), autoregressive (XLnet), masked&permuted (Mp-net), text-text (T5) and large language models (Llama2 and Llama3) under low source settings. We find that they perform competitively on explicit harassment binary detection, however, their performance is substantially lower on harassment and denigration multi-classification tasks. Therefore, we propose an emotion-adaptive training framework (EAT) that helps transfer knowledge from the domain of emotion detection to the domain of cyberbullying detection to help detect indirect cyberbullying events. EAT consistently improves the average macro F1, precision and recall by 20% in cyberbullying detection tasks across nine transformer-based models under low-resource settings. Our claims are supported by intuitive theoretical insights and extensive experiments.
Measuring and Forecasting Conversation Incivility: the Role of Antisocial and Prosocial Behaviors
This paper focuses on the task of measuring and forecasting incivility in conversations following replies to hate speech. Identifying replies that steer conversations away from hatred and elicit civil follow-up conversations sheds light into effective strategies to engage with hate speech and proactively avoid further escalation. We propose new metrics that take into account various dimensions of antisocial and prosocial behaviors to measure the conversation incivility following replies to hate speech. Our best metric aligns with human perceptions better than prior work. Additionally, we present analyses on a) the language of antisocial and prosocial posts, b) the relationship between antisocial or prosocial posts and user interactions, and c) the language of replies to hate speech that elicit follow-up conversations with different incivility levels. We show that forecasting the incivility level of conversations following a reply to hate speech is a challenging task. We also present qualitative analyses to identify the most common errors made by our best model.
A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media
Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies.
Responsible RecSys by Design: Approximation Algorithms for Calibrated Recommendations with Sponsored Items
Calibrated Recommendation Systems (CRS) balance user preferences with constraints like diversity, fairness, and novelty to create inclusive recommendation lists. However, existing research often overlooks the mandatory inclusion of sponsored items, assuming unrestricted product selection. In practice, sponsored items, paid for by advertisers, must be included, which can conflict with CRS goals when advertisers' priorities misalign with system objectives. This paper addresses this gap by formulating CRS with sponsored items as a combinatorial optimization problem. We develop efficient approximation algorithms to generate the most calibrated recommendation lists while meeting sponsorship requirements.
Examining the Makeup of Media Trigger Warnings Online
In today’s digital landscape, the prevalence of sensitive online content has made trigger warnings essential. These warnings inform viewers that the content they are about to see contains sensitive artifacts (e.g. violence). This paper studies the use of trigger warnings, exploiting data from two major platforms: Does the Dog Die, a crowdsourcing trigger warnings platform, and IMDb, a media database. We first study how different media types (e.g. films, video games, and TV shows) are labeled with varying trigger warnings and the different co-occurrence patterns among different trigger warnings. We also discover controversy surrounding certain trigger warnings, with inconsistent opinions stated by different people. We further show that different jurisdictions (e.g. USA vs. UK) assign different content ratings (e.g. R-18) for the same media, even when the same trigger warnings are present. Finally, we develop automatic detectors to identify trigger warnings from IMDb text. We achieve F1 scores exceeding 0.7 for all 10 selected trigger warnings.
Paths of A Million People: Extracting Life Trajectories from Wikipedia
The life trajectories of notable people have been studied to pinpoint the times and places of significant events such as birth, death, education, marriage, competition, work, speeches, scientific discoveries, artistic achievements, and battles. Understanding how these individuals interact with others provides valuable insights for broader research into human dynamics. However, the scarcity of trajectory data in terms of volume, density, and inter-person interactions, limits relevant studies from being comprehensive and interactive. We mine millions of biography pages from Wikipedia and tackle the generalization problem stemming from the variety and heterogeneity of the trajectory descriptions. Our ensemble model COSMOS, which combines the idea of semi-supervised learning and contrastive learning, achieves an F1 score of 85.95%. For this task, we also create a hand-curated dataset, WikiLifeTrajectory, consisting of 8,852 (person, time, location) triplets as ground truth. Besides, we perform an empirical analysis on the trajectories of 8,272 historians to demonstrate the validity of the extracted results. To facilitate the research on trajectory extractions and help the analytical studies to construct grand narratives, we make our code, the million-level extracted trajectories, and the WikiLifeTrajectory dataset publicly available.
Quantifying the Spread of Online Incivility in Brazilian Politics
Incivility refers to behaviors that violate collective norms and disrupt cooperation within the political process. Although large-scale online data and automated techniques have enabled the quantitative analysis of uncivil discourse, prior research has predominantly focused on impoliteness or toxicity, often overlooking other behaviors that undermine democratic values. To address this gap, we propose a multidimensional conceptual framework encompassing Impoliteness (IMP), Physical Harm and Violent Political Rhetoric (PHAVPR), Hate Speech and Stereotyping (HSST), and Threats to Democratic Institutions and Values (THREAT). Using this framework, we measure the spread of online political incivility in Brazil using approximately 5 million tweets posted by 2,307 political influencers during the 2022 Brazilian general election. Through statistical modeling and network analysis, we examine the dynamics of uncivil posts at different election stages, identify key disseminators and audiences, and explore the mechanisms driving the spread of uncivil information online. Our findings indicate that impoliteness is more likely to surge during election campaigns. In contrast, the other dimensions of incivility are often triggered by specific violent events. Moreover, we find that left-aligned individual influencers are the primary disseminators of online incivility in the Brazilian Twitter/X sphere and that they disseminate not only direct incivility but also indirect incivility when discussing or opposing incivility expressed by others. They relay those content from politicians, media agents, and individuals to reach broader audiences, revealing a diffusion pattern mixing the direct and two-step flows of communication theory. This study offers new insights into the multidimensional nature of incivility in Brazilian politics and provides a conceptual framework that can be extended to other political contexts.
Cyber Food Swamps: Investigating the Impacts of Online-to-Offline Food Delivery Platforms on Healthy Food Choices
Online-to-offline (O2O) food delivery platforms have greatly expanded urban residents' access to a wide range of food options by allowing convenient ordering from distant food outlets. However, concerns persist regarding the nutritional quality of delivered food, particularly as the impact of O2O food delivery platforms on users’ healthy food remains unclear. This study leverages large-scale empirical data from a leading O2O delivery platform to comprehensively analyze online food choice behaviors and how they are influenced by the online exposure to fast food restaurants, i.e., online food environment. Our analyses reveal significant variations in food preferences across demographic groups and city sizes, where male, low-income, and younger users are more likely to order fast food via O2O platforms. Besides, we also perform a comparative analysis on the food exposure differences in offline and online environments, confirming that the extended service ranges of O2O platforms can create larger "cyber food swamps". Furthermore, regression analysis highlights that a higher ratio of fast food orders is associated with "cyber food swamps", areas characterized by a higher proportion of accessible fast food restaurants. A 10% increase in this proportion raises the probability of ordering fast food by 22.0%. Moreover, a quasi-natural experiment substantiates the long-term causal effect of online food environment changes on healthy food choices. These findings underscore the need for O2O food delivery platforms to address the health implications of online food choice exposure, offering critical insights for stakeholders aiming to improve dietary health among urban populations.
AOTree: Aspect Order Tree-Based Model for Explainable Recommendation
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users’ decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user’s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.
Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News
Increasing cycling for transportation or recreation can boost public health and reduce the environmental impacts of vehicles. However, news agencies' ideologies and reporting styles often influence public perception of cycling. For example, if news agencies overly report cycling accidents, it may make people perceive cyclists as "dangerous," reducing the number of opting to cycle. Additionally, a decline in cycling can result in less government funding for safe infrastructure. In this paper, we develop a novel prompting method to detect the perceived perception of cyclists within news headlines. To support this, we introduce a new dataset called "Bike Frames," which contains 31,480 news headlines and 1,500 human annotations. Our analysis focuses on 11,385 headlines from the United States. We also propose the BikeFrame Chain-of-Code (CoC) framework, which predicts cyclist perception, identifies accident-related headlines, and determines fault. This framework uses structured pseudocode to represent logical reasoning steps and incorporates news agency bias to enhance prediction accuracy, outperforming traditional chain-of-thought methods used in large language models. Most importantly, we find that incorporating news bias information significantly impacts performance, improving the average F1 score from .739 to .815. Finally, we conduct a comprehensive case study on U.S. news headlines, revealing differences in reporting between mainstream news agencies and cycling-specific websites, as well as variations in coverage based on the gender of cyclists. WARNING: This paper contains descriptions of accidents and death.
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
Emojis, which encapsulate semantics beyond words or phrases, have become prevalent in social network communications. This has spurred increasing scholarly interest in exploring their attributes and functionalities. However, emoji-related research and application face two primary challenges. First, researchers typically rely on crowd-sourcing to annotate emojis in order to understand their sentiments, usage intentions, and semantic meanings. Second, subjective interpretations by users can often lead to misunderstandings of emojis and cause a communication barrier. Large Language Models (LLMs) have achieved significant success in various annotation tasks, with ChatGPT demonstrating expertise across multiple domains. In our study, we assess ChatGPT's effectiveness in handling previously emoji-annotated and downstream tasks. Our objective is to validate the hypothesis that ChatGPT can serve as an alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications. Our findings indicate that ChatGPT has extensive knowledge of emojis. It is adept at explaining the meaning of emojis across various application scenarios and demonstrates the potential to replace human annotators in a range of tasks.
Same Vaccine, Different Voices: A Cross-Modality Analysis of HPV Vaccine Discourse on Social Media
Despite the proven efficacy of HPV vaccines, uptake remains limited in many regions, including China. This study investigates how health beliefs and emotional responses evolve across text-, audio-, and video-based platforms by analyzing data from three representative platforms in China, including 273,357 posts from Weibo (text-based), 1,228 podcasts from Ximalaya (audio-based), and 1,225 videos from Douyin (video-based) from July 2018 to March 2023. The comparisons are conducted under four dimensions as suggested by the Health Belief Model (HBM), including susceptibility, severity, benefits, and barriers. Our findings reveal distinct modality-specific patterns. For instance, a text-based platform tends to amplify barriers and negativity, an audio-based platform enables balanced and sustained discussions, and a video-based platform highlights personal anecdotes and drives rapid sentiment shifts. By highlighting these modality-specific differences and addressing potential cross-modal incongruities at the content level, we provide actionable insights for public health communicators, policymakers, and platform designers to tailor strategies, foster informed decision-making, and ultimately enhance HPV vaccine uptake in complex social media ecosystems.
Empowering LLMs to Synthesize AI and Human Intelligence for Explainable Public Health Misinformation Detection on Social Media
This paper studies a critical problem of explainable public health misinformation detection on social media, where clear explanations are essential for enhancing user understanding and trust, surpassing the limitations of black-box misinformation detection results. To tackle this problem, there is a growing trend of leveraging collective intelligence from diverse intelligence sources, such as deep neural networks (DNNs), human intelligence, and large language models (LLMs). However, integrating hybrid intelligence from different sources remains a challenge: DNNs excel in accurate and efficient classification, crowd workers provide contextual understanding and readable explanations, and LLMs offer extensive domain knowledge and advanced language generation. Moreover, current crowdsourcing and human-AI collaboration methods mainly focus on aggregating misinformation detection labels using traditional measures like consistency, often overlooking more complex and challenging inputs like textual explanations. We propose SynthX, a collective intelligence framework that incorporates a holistic prompting design to harness the language and reasoning capabilities of LLMs for synthesizing diverse detection and explanation results. It also integrates a novel estimation theory-LLM hybrid approach to assess the varying reliability of detection results from different intelligence sources. Our evaluation on a real-world social media misinformation dataset demonstrates that SynthX consistently outperforms a rich set of state-of-the-art baselines in both detection accuracy and explanation quality.
Analyzing Digital Polarization on Hijab: A Dataset of Annotated YouTube Comments
This study presents an analysis of digital polarization on the topic of the Hijab by examining YouTube comments in Arabic. Employing a novel dataset of around 10K annotated comments, this research investigates the digital discourse using seven labels: Stance, Use of Sarcasm, Argumentation, Cordiality, Offensiveness, Hopefulness, and Apparent Gender of Commenters. The findings reveal significant insights into gender dynamics and the prevalence of specific rhetorical strategies within the debate. This study contributes to the broader field of polarization and argument mining, offering a unique lens on the intersection of digital culture and societal issues in the Arab context.
Telegram as a Battlefield: Kremlin-Related Communications During the Russia-Ukraine Conflict
Telegram emerged as a crucial platform for both parties during the conflict between Russia and Ukraine. Per its minimal policies for content moderation, Pro-Kremlin narratives and potential misinformation were spread on Telegram, while anti-Kremlin narratives with related content were also propagated, such as war footage, troop movements, maps of bomb shelters, and air raid warnings. This paper presents a dataset of posts from both pro-Kremlin and anti-Kremlin Telegram channels, collected over a period spanning a year before and a year after the Russian invasion. The dataset comprises 404 pro-Kremlin channels with 4,109,645 posts and 114 anti-Kremlin channels with 1,117,768 posts. We provide details on the data collection process, processing methods, and dataset characterization. Lastly, we discuss the potential research opportunities this dataset may enable researchers across various disciplines.
UKTwitNewsCor: A Dataset of Online Local News Articles for the Study of Local News Provision
In this paper, we present UKTwitNewsCor, a comprehensive dataset for understanding the content production, dissemination, and audience engagement dynamics of online local media in the UK. It comprises over 2.5 million online news articles published between January 2020 and December 2022 from 360 local outlets. The corpus represents all articles shared on Twitter by the social media accounts of these outlets. We augment the dataset by incorporating social media performance metrics for the articles at the tweet level. We further augment the dataset by creating metadata about content duplication across domains. Alongside the article dataset, we supply three additional datasets: a directory of local media web domains, one of UK Local Authority Districts, and one of digital local media providers, providing statistics on the coverage scope of UKTwitNewsCor. Our contributions enable comprehensive, longitudinal analysis of UK local media, news trends, and content diversity across multiple platforms and geographic areas. In this paper, we describe the data collection methodology, assess the dataset geographic and media ownership diversity, and outline how researchers, policymakers, and industry stakeholders can leverage UKTwitNewsCor to advance the study of local media.
You Have the Floor: A Speaker-Aligned Corpus Derived from the Congressional Record
The United States Congressional Record serves as a comprehensive archive of legislative discourse, yet its sheer volume and unstructured format pose significant challenges for researchers interested in analyzing political language, speaker behavior, and ideological framing. This paper presents a new dataset that organizes Congressional speeches by individual speakers. Data was obtained using a mixture of the Congress.gov Application Programming Interface (API) and web-scraping techniques to retrieve the full text of the Congressional Record. After extracting roll-call votes and standardizing the transcripts to remove noisy artifacts and normalize formatting, each speaker is separated into individual files and annotated with metadata including name, political affiliation, years active, district or state represented, and professional social media accounts, if known. This enables fine-grained analysis of rhetorical patterns and linguistic strategies across different political groups as well as time periods. By making the dataset publicly available, we aim to support interdisciplinary research utilizing natural language processing (NLP).
This paper introduces a comprehensive dataset of news articles sourced from ESH Médias, a prominent local press agency in Romandy, the French-speaking region of Switzerland. The dataset encompasses all articles published on their digital platforms from January 2015 through June 2022. With over 130,000 articles written in French, this dataset offers a rich insight into local news from the French-speaking cantons of Switzerland. The articles cover a diverse range of topics and provide valuable material for Natural Language Processing and media studies. To respect privacy and legal considerations, journalists' names have been anonymized, and the dataset is made available for research purposes under a specific agreement with ESH Médias. The dataset adheres to the FAIR principles, and a detailed datasheet is provided to facilitate its use. The dataset is accessible via a DOI link.
A Dataset for Analysing News Framing in Chinese Media
Framing is an essential device in news reporting, allowing writers to influence public perceptions of current affairs. While automatic news framing detection datasets exist in various languages, none focus on news framing in the Chinese language, which presents unique challenges with complex character meanings and unique linguistic features. This study introduces the first Chinese News Framing dataset, to be used as either a stand-alone dataset or a supplementary resource to the SemEval-2023 task 3 dataset. We detail its creation and conduct baseline experiments to demonstrate the need for such a dataset and create benchmarks for future research, providing results obtained through fine-tuning XLM-RoBERTa-Base and using GPT-4o in the zero-shot setting. We find that GPT-4o performs significantly worse than fine-tuned XLM-RoBERTa across all languages. For the Chinese language, we obtain an F1-micro (the performance metric for SemEval task 3, subtask 2) score of 0.719 using only samples from our Chinese News Framing dataset and a score of 0.753 when we augment the SemEval dataset with Chinese news framing samples. With positive news frame detection results, this dataset is a valuable resource for detecting news frames in the Chinese language and is a useful supplement to the SemEval-2023 task 3 dataset.
ArDia: Improving Arabic Dialectal Language Classification Using a Novel Dataset
Despite Arabic being one of the most widely spoken languages, there is a scarcity of available dialectal Arabic data. In this paper, we address this challenge by proposing a novel approach to data collection through the main use of video captions from TikTok, and other resources such as dictionaries and articles, resulting in the creation of the ArDia dataset. To the best of our knowledge, the ArDia dataset is the largest labeled dialectal Arabic dataset, containing over 900,000 examples, each labeled with its respective dialect. We further leverage this dataset to pretrain transformer-based models, ArDiaBERT and ArDiaGPT. Due to a lack of research on the Arabic models, we present a comprehensive study of Arabic dialect identification using the ArDia dataset on the dialect identification task.
TeleScope A Longitudinal Dataset for Investigating Online Discourse and Information Interaction on Telegram
Telegram is a globally popular instant messaging platform known for its strong emphasis on security, privacy, and unique social networking features. It has recently emerged as the host for various cross-domain analysis and research works, such as social media influence, propaganda studies, and extremism. This paper introduces TeleScope, an extensive dataset suite that, to our knowledge, is the largest of its kind. It comprises metadata for about 500K Telegram channels and downloaded message metadata for about 71K public channels, accounting for around 120M crawled messages. We also release channel connections and user interaction data built using Telegram’s message-forwarding feature to study multiple use cases, such as information spread and message-forwarding patterns. In addition, we provide data enrichments, such as language detection, active message posting periods for each channel, and Telegram entities extracted from messages, that enable online discourse analysis beyond what is possible with the original data alone. The dataset is designed for diverse applications, independent of specific research objectives, and sufficiently versatile to facilitate the replication of social media studies comparable to those conducted on platforms like X (formerly Twitter).
WikiReddit: Tracing Information and Attention Flows Between Online Platforms
The World Wide Web is a complex interconnected digital ecosystem, where information and attention flow between platforms and communities throughout the globe. These interactions co-construct how we understand the world, reflecting and shaping public discourse. Unfortunately, researchers often struggle to understand how information circulates and evolves across the web because platform-specific data is often siloed and restricted by linguistic barriers. To address this gap, we present a comprehensive, multilingual dataset capturing all Wikipedia mentions and links shared in posts and comments on Reddit 2020–2023, excluding those from private and NSFW subreddits. Each linked Wikipedia article is enriched with revision history, page view data, article ID, redirects, and Wikidata identifiers. Through a research agreement with Reddit, our dataset ensures user privacy while providing a query and ID mechanism that integrates with the Reddit and Wikipedia APIs. This enables extended analyses for researchers studying how information flows across platforms. For example, Reddit discussions use Wikipedia for deliberation and fact-checking which subsequently influences Wikipedia content, by driving traffic to articles or inspiring edits. By analyzing the relationship between information shared and discussed on these platforms, our dataset provides a foundation for examining the interplay between social media discourse and collaborative knowledge consumption and production.
SAID: A Social Media AI-generated Interface Dataset Using Prompt Engineering Methods Focused On Accessibility
We present SAID (Social Media AI-generated Interface Dataset), a systematically curated collection of 240 social media profile interfaces generated through controlled prompt engineering focused on accessibility. As AI tools reshape interface design practices, understanding how these systems interpret and implement accessibility requirements in social media interfaces becomes more and more important. Through six distinct prompt categories examining both generic and specific accessibility requirements, our dataset captures how AI systems interpret and implement accessibility features across visual and motor impairment dimensions. The dataset combines complete interface designs in multiple formats (PNG and SVG), detailed prompt engineering methodology, and comprehensive documentation of interface components such as social identity presentation, content engagement, navigation, and interactive elements. SAID enables novel research directions from understanding AI's role in shaping accessible social media experiences to examining how automated design tools can support more inclusive social interactions.
Practical Datasets for Analyzing LLM Corpora Derived from Common Crawl
Large language models (LLMs) rely heavily on web-derived training datasets, yet understanding how filtering and curation decisions affect these datasets remains challenging. This paper presents two complementary datasets designed to enable systematic analysis of LLM training data composition. The first dataset captures domain-level statistics across 96 Common Crawl snapshots, providing baseline data about web content distribution before filtering. The second dataset contains standardized URL information from three major LLM training corpora (C4, Falcon RefinedWeb, and CulturaX), allowing researchers to analyze how different filtering approaches affect content inclusion. By making these datasets publicly available in a consistent format, we aim to (1) facilitate research into training data composition, (2) enable systematic auditing of filtering effects, and (3) support more transparent approaches to dataset development. Our datasets can help researchers investigate questions related to content diversity, source representation, and the impact of different filtering decisions on training data composition. Overall, this work provides a foundation for understanding how curation choices shape the content that ultimately trains widely-deployed language models.
Statewise: Human Identity Investigator for the United States
Self-reported biographical strings on social media profiles provide a powerful tool to study personal identity. We present Statewise, a dataset based on 50 million unique Twitter user profiles over a 12 year period identified to be in the United States. Users within this dataset can be accurately partitioned into 52 states/territories at each observation, allowing queries into state-specific language choices over time. We report on the major design decisions underlying Statewise, including the methodology behind the location detection system and measurements of user/state transitions across time. We demonstrate the power of Statewise to study the relative prevalences of different token groups, showing clear and consistent regional differences in language usage. We analyze emoji usage by comparing inclusion rates against external state-level statistics, finding that emoji inclusion shares a significant correlation with state unemployment and poverty rates. Finally, we use Gini coefficients as a measure of token usage inequality across all observed territories and demonstrate a clear stratification based on token content.
UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections
Misleading narratives play a crucial role in shaping public opinion during elections, as they can influence how voters perceive candidates and political parties. This entails the need to detect these narratives accurately. To address this, we introduce the first taxonomy of common misleading narratives that circulated during recent elections in Europe. Based on this taxonomy, we construct and analyse UKElectionNarratives: the first dataset of human-annotated misleading narratives which circulated during the UK General Elections in 2019 and 2024. We also benchmark Pre-trained and Large Language Models (focusing on GPT-4o), studying their effectiveness in detecting election-related misleading narratives. Finally, we discuss potential use cases and make recommendations for future research directions using the proposed codebook and dataset.
MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkers
Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users. These platforms expose users to harmful content, ranging from clickbait or physical harms to hate or misinformation. Yet, we lack a comprehensive understanding and measurement of online harm on short video platforms. Toward this end, we present two large-scale datasets of multi-modal and multi-categorical online harm: (1) 60,906 systematically selected potentially harmful YouTube videos and (2) 19,422 videos annotated by three labeling actors: trained domain experts, GPT-4-Turbo (using 14 image frames, 1 thumbnail, and text metadata), and crowdworkers (Amazon Mechanical Turk master workers). The annotated dataset includes both (a) binary classification (harmful vs. harmless) and (b) multi-label categorizations of six harm categories: Information, Hate and harassment, Addictive, Clickbait, Sexual, and Physical harms. Furthermore, the annotated dataset provides (1) ground truth data with videos annotated consistently across (a) all three actors and (b) the majority of the labeling actors, and (2) three data subsets labeled by individual actors. These datasets are expected to facilitate future work on online harm, aid in (multimodal) classification efforts, and advance the identification and potential mitigation of harmful content on video platforms.
A Telegram Dataset of Propaganda and its Moderation
Messaging applications like Telegram have evolved into de facto social networking platforms as they add features like broadcast channels and large groups. Yet, research on these aspects of Telegram is sparse compared to more traditional social media platforms. In this paper, we present a dataset of Telegram messages collected using the export API that returns channel histories, complemented by messages collected in real-time. This dual collection methodology allows us to label deleted messages, i.e., messages that are present in the real-time dataset but not the historical dataset. Additionally, we provide labels indicating whether messages have been sent by accounts belonging to one of two distinct propaganda networks. We provide experiments that show how this rich dataset of Telegram messages can be used to study moderation in Telegram, stances and trends on different topics, and to shed light on malicious behaviours present on Telegram. Finally, we outline other use cases where our dataset could help the research community better understand Telegram as a social network.
News on TikTok: An Annotated Dataset of TikTok Videos from German-Speaking News Outlets in 2023
TikTok has emerged as a leading social media platform with increasing relevance for the consumption and distribution of news, especially for younger age groups. Despite its growing relevance, analyses of how traditional news outlets produce content for the platform and maintain journalistic news values are limited. Moreover, there are few large-scale datasets that are suitable for tracing larger journalistic trends and developing approaches for the automated annotation of multimodal content. This paper addresses these gaps and introduces “News on TikTok,” an annotated dataset of 8,623 TikTok videos published by 18 major German-speaking news outlets in 2023. Combining metadata with human-annotated data of 25 variables (incl. the presence of visual, auditory, and interactive elements, and journalistic news values), our dataset makes three significant contributions: First, it enables extensive analyses of news characteristics on TikTok. Second, it provides ground truth data to develop and validate automated tools for multimodal content analyses. Third, it offers a comprehensive guide for generating datasets with similar research interests.
Multi-Platform Aggregated Dataset of Online Communities (MADOC)
The Multi-platform Aggregated Dataset of Online Communities (MADOC) is a comprehensive dataset that facilitates computational social science research by providing a unified, standardized dataset for cross-platform analysis of online social dynamics. MADOC aggregates and standardizes data from four distinct platforms: Bluesky, Koo, Reddit, and Voat, spanning from 2012 to 2024. The dataset includes 18.9 million posts, 236 million comments, and data from 23.1 million unique users across all platforms, with a particular focus on understanding community dynamics, user migration patterns, and the evolution of toxic behavior across platforms. By providing standardized data structures and FAIR-compliant access through Zenodo and corresponding Python and R packages, MADOC enables researchers to conduct comparative analyses of user behavior, interaction networks, and content sentiment across diverse social media environments. The unique value of the dataset lies in its cross-platform scope, standardized structure, and rich metadata, making it particularly suitable for studying societal phenomena such as community formation, toxic behavior propagation, and user migration patterns in response to platform moderation policies.
Mobility Networked Time-Series Forecasting Benchmark Datasets
Human mobility is crucial for urban planning (e.g., public transportation) and epidemic response strategies. However, existing research often neglects integrating comprehensive perspectives on spatial dynamics, temporal trends, and other contextual views due to the limitations of existing mobility datasets. To bridge this gap, we introduce MOBINS (MOBIlity Networked time Series), a novel dataset collection designed for networked time-series forecasting of dynamic human movements. MOBINS features diverse and explainable datasets that capture various mobility patterns across different transportation modes in four cities and two countries and cover both transportation and epidemic domains at the administrative area level. Our experiments with nine baseline methods reveal the significant impact of different model backbones on the proposed six datasets. We provide a valuable resource for advancing urban mobility research.
Can-PolNews: A Multi-Platform Dataset of Political Discourse in Canada
For many societies, social media has become the primary venue for encountering and engaging with political discourse. But whereas ideas and conversations span multiple communities and move fluidly between different platforms, publicly available datasets are often limited to a single platform. In this paper, we present a multi-platform social media dataset focused on political discourse in Canada, spanning January 1st, 2023, to January 1st, 2025. Our dataset contains all content posted to social media by Canadian news media (national,provincial, and local) and Canadian politicians (federal and provincial) for 1,852 unique accounts across four major platforms popular among Canadians: Instagram, X/Twitter, TikTok, and YouTube. Politicians are labeled by their political party affiliations and provinces, facilitating comparative analysis of regional political trends and ideological affinities. By covering a two-year time frame, this dataset, containing more than 5 million posts with a normalized schema across four platforms, enables researchers to analyze patterns and trends of digital political engagement, and the interplay of news and political elites on social media in an established democracy.
OCanada-TVNews: A Two-Year Dataset for Gender and Topic Diversity in Canadian TV News Broadcasts
This paper presents OCanada-TVNews, a two-year dataset of Canadian TV news broadcasts, from four major channels: CBC The National, CTV The National, Global News, and RadioCanada Info, retrieved from channels’ YouTube playlists. The dataset consists of 3982 videos, in total 999 hours. Each video is processed through a systematic pipeline that involves audio extraction, speaker diarization, gender detection, and topic classification. In addition, for each video, metadata containing user engagements is also provided. By merging speaker segments with gender labels and topic categories, this dataset offers a detailed view of who speaks and what is discussed on a daily basis. An exploratory analysis highlights persistent disparities in gender representation and provides insights into the topics that dominate coverage. OCanada-TVNews serves as a basis for a further study of media diversity, content trends, and potential biases in broadcast journalism.
Labeled Datasets for Research on Information Operations
Social media platforms have become a hub for political activities and discussions, democratizing participation in these endeavors. However, they have also become an incubator for manipulation campaigns, like information operations (IOs). Some social media platforms have released datasets related to such IOs originating from different countries. However, we lack comprehensive control data that can enable the development of IO detection methods. To bridge this gap, we present new labeled datasets about 26 campaigns, which contain both IO posts verified by a social media platform and over 13M posts by 303k accounts that discussed similar topics in the same time frames (control data). The datasets will facilitate the study of narratives, network interactions, and engagement strategies employed by coordinated accounts across various campaigns and countries. By comparing these coordinated accounts against organic ones, researchers can develop and benchmark IO detection algorithms.
MagnetDB: A Longitudinal Torrent Discovery Dataset with IMDb-Matched Movies and TV Shows
BitTorrent remains a prominent channel for illicit distribution of copyrighted material, yet the supply side of such content remains understudied. We introduce MagnetDB, a longitudinal dataset of torrents discovered through the BitTorrent DHT between 2018 and 2024, containing more than 28.6 million torrents and metadata of more than 950 million files. While our primary focus is on enabling research based on the supply of pirated movies and TV shows, the dataset also encompasses other legitimate and illegitimate torrents. By applying IMDb-matching and annotation to movie and TV show torrents, MagnetDB facilitates detailed analyses of pirated content evolution in the BitTorrent network. Researchers can leverage MagnetDB to examine distribution trends, subcultural practices, and the gift economy within piracy ecosystems. Through its scale and temporal scope, MagnetDB presents a unique opportunity for investigating the broader dynamics of BitTorrent and advancing empirical knowledge on digital piracy.
ThatiAR: Subjectivity Detection in Arabic News Sentences
In this study, we present the first large dataset, ThatiAR, for subjectivity detection in Arabic, consisting of ~3.6K manually annotated sentences, and GPT-4o based explanations. In addition, we include instructions (both in English and Arabic) to facilitate LLM based fine-tuning. We provide an in-depth analysis of the dataset, annotation process, and extensive benchmark results, including PLMs and LLMs. Our analysis of the annotation process highlights that annotators were strongly influenced by their political, cultural, and religious backgrounds, especially at the beginning of the annotation process. The experimental results suggest that LLMs with in-context learning provide better performance. We release the dataset and resources to the community.
NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models
Online texts with toxic content are a clear threat to the users on social media in particular and society in general. Although many platforms have adopted various measures (e.g., machine learning-based hate-speech detection systems) to diminish their effect, toxic content writers have also attempted to evade such measures by using cleverly modified toxic words, so-called human-written text perturbations. Therefore, to help build automatic detection tools to recognize those perturbations, prior methods have developed sophisticated techniques to generate diverse adversarial samples. However, we note that these ``algorithm"-generated perturbations do not necessarily capture all the traits of ``human"-written perturbations. Therefore, in this paper, we introduce a novel, high-quality dataset of human-written perturbations, named as NoisyHate, that was created from real-life perturbations that are both written and verified by human-in-the-loop. We show that perturbations in NoisyHate have different characteristics than prior algorithm-generated toxic datasets show and thus can be particularly useful to help develop better toxic speech detection solutions. We also provide basic benchmark on the potential utilities of NoisyHate in perturbation normalization and understanding tasks. Both dataset and source code are publicly available.
Mapping the Climate Change Landscape on TikTok
Social media platforms shape climate action discourse. Mapping these online conversations is essential for effective communication strategies. TikTok’s climate discussions are particularly relevant given its young, climate-concerned audience. In this work, we collect the first TikTok dataset on climate topics. We collected 590K videos from 14K creators along with their follower networks. By applying topic modeling to the video descriptions, we map the topics discussed on the platform on a climate taxonomy that we construct by consolidating existing categorizations. Results show TikTok creators primarily approach climate through the angle of lifestyle and dietary choices. By examining semantic connections between topics, we identified non-climate ”gateway” topics that could draw new audiences into climate discussions.
Variational Inference of Parameters in Opinion Dynamics Models
Modeling human behavior through the lens of online social networks presents both a significant opportunity and a challenge for understanding complex social phenomena, such as misinformation spread, opinion formation and polarization. While agent-based models (ABMs) are widely used for studying these social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (PGABMs): we start by synthesizing a probabilistic generative model from the ABM rules. Then, we transform the inference process into an optimization problem suitable for automatic differentiation. In particular, we use the Gumbel-Softmax reparameterization for categorical agent attributes and Stochastic Variational Inference for parameter estimation. Moreover, we explore the trade-offs of using variational distributions with different complexities: Normal distributions and Normalizing Flows. We validate our method on a bounded confidence model with agent roles (leaders and followers), by estimating both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic (200 categorical agent-level roles) parameters more accurately than simulation-based and MCMC methods.
Ethnic Diversity and Spatial Dynamics in Google Maps Reviews: Insights from a German Border City
Location-based online services, such as Google Maps, provide a valuable lens for examining social dynamics in both online (virtual) and offline (physical) spaces. In particular, online reviews offer insights into how cultural or ethnic differences shape mobility patterns, place preferences, and public expression. This study analyzes the spatial behaviour of select ethnic groups in a German border city by integrating Google Maps reviews with demographic data. Using comparative analysis, we identify patterns in place engagement and disparities in urban space usage across different population groups. The findings highlight how socio-demographic factors influence the frequency and types of places visited, revealing gaps in urban accessibility. These insights demonstrate the potential of Google Maps data for understanding socio-spatial dynamics and inform strategies for more inclusive and data-driven urban planning.
Pathways to Conspiracy Theorizing on YouTube in Japan
This study investigates how viewers and creators on Japanese YouTube channels progress towards conspiracy theories. By categorizing channels based on ideological or financial motives and analyzing engagement metrics such as views, likes, and comments, we find that channels driven by monetization, particularly Monetized Conspiracists, promote conspiracy theories more vigorously. This indicates that financial incentives are a crucial factor in the proliferation of such content. Channels that package conspiracy theories in formats like entertainment or spirituality serve as gateways, facilitating viewers' progression towards more extreme conspiracy-laden content. Understanding these pathways is vital for crafting strategies to counteract the spread of conspiracy theories on social media.
Knowledge-Augmented Intelligent Bot Framework for Enabling Accessible Design
This paper proposes the development of a WCAG-compliant chatbot capable of generating multimodal content to enhance usability for all users. While LLM-based chatbots excel in generating varied responses, they often struggle with ambiguous or incomplete queries, leading to misaligned outputs. We introduce a framework that formulates domain-specific, persona-driven follow-up questions to clarify ambiguities, utilizing knowledge graphs and human feedback. The system refines queries before generating responses by employing a domain-Specific Multilayer Hierarchical Relational Graph (MHRG) to model user intent. Our preliminary evaluations indicate that the Accessibility Bot improves response relevance and quality as compared to existing techniques.
Opposites Attract? Ambivalence in Distinguishing Real and Fake News and Predicting their Spread
This study expands the understanding of linguistic cues in fake news by exploring ambivalent language's role in distinguishing real from fake news and its impact on news spread. Unlike prior research focusing on positive or negative language separately, this work hypothesizes that fake news may exhibit higher ambivalence, aligning with its association with high-arousal emotions and writers' efforts to attract attention. Ambivalence, traditionally viewed as the co-occurrence of conflicting positive and negative elements, is extended here to include diverse dimensions such as textual, temporal, psychological, and content-related ambivalence.
Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic - a Case Study on Media Bias
Defining complex, evolving concepts in academic research and extracting clear taxonomies from many publications is challenging. To streamline systematic reviews and capture shifts in conceptual understanding, we present our ongoing work on TaxoMatic - a framework leveraging Large Language Models (LLMs) to automate definition extraction from academic literature. The framework encompasses data collection, relevance classification to identify papers with definitions, and definition extraction using LLMs. As a first case study, we tested our relevancy evaluation component on 2,398 articles on media bias, a domain particularly rich in varying definitions and sub-concepts. Then, we evaluated our definition extraction component on manually reviewed papers, yielding 123 definitions from 113 relevant articles. Among five tested LLMs, Claude-3-sonnet achieved the highest F1 score (0.381) for relevance classification and demonstrated a median cosine similarity of 0.557 for definition extraction with role prompting. Future directions include improving relevance classification, expanding ground truth datasets, and applying this framework to other domains, potentially enhancing conceptual clarity across disciplines.
KeepA(n)I: Social Stereotypes in and Social Norms for Computer Vision
The KeepA(n)I platform facilitates the auditing of computer vision systems that tag images, which aid visual communication on the Web and social media, from content moderation to the development of new apps and tools. In particular, KeepA(n)I enables a broad set of stakeholders to scrutinize a process of interest that embeds an image tagger for issues of social stereotyping, while also examining the social norms that humans apply to the observed AI behaviors. KeepA(n)I’s approach, and its use of the power of the crowd, can aid the stakeholders in receiving responses to both descriptive and normative questions (i.e., which stereotyping behaviors are observed and if they are considered problematic by a given “crowd” for an intended context). We provide an overview of the platform, its key features, and a discussion via a use case on the diverse set of stakeholders that can benefit from it.