X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums

Authors

  • Anna Guimarães Max Planck Institute for Informatics
  • Gerhard Weikum Max Planck Institute for Informatics

DOI:

https://doi.org/10.1609/icwsm.v15i1.18050

Keywords:

Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Qualitative and quantitative studies of social media, Trend identification and tracking; time series forecasting, Centrality/influence of social media publications and authors

Abstract

Most online discussion forums capture user feedback in the form of "likes'' and other similar signals, but limit this to positive feedback. A few forums, most notably Reddit, offer both upvotes and downvotes. Reddit posts that received a large number of both upvotes and downvotes receive an explicit "controversiality'' marker, while heavily downvoted posts are hidden from the standard view of the discussion, and only shown upon explicit clicks. This paper aims at understanding the nature and role of controversial posts in Reddit, considering four subreddits of very different natures: US politics, World politics, Relationships and Soccer. We design a feature space and devise a classifier to predict the occurrence of a controversial post given a prefix of a path in a discussion thread. Our findings include that these classifiers exhibit different behaviors in the four subreddits, and we identify key features for the respective cases. An in-depth analysis indicates that controversial posts do not arise as troll-like behavior, but are often due to a polarizing topic (mostly in US politics), off-topic content, or mentions of individual entities such as soccer players or clubs.

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Published

2021-05-22

How to Cite

Guimarães, A., & Weikum, G. (2021). X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 163-172. https://doi.org/10.1609/icwsm.v15i1.18050