Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features

By Jack Hessel, Lillian Lee. 2019

83 citations recorded by the source corpus.

Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback. Our inclusion of the word “community” here is deliberate: what is controversial to some audiences may not be so to others. Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts, leveraging features drawn from both the textual content and the tree structure of the early comments that initiate the discussion. We find that even when only a handful of comments are available, e.g., the first 5 comments made within 15 minutes of the original post, discussion features often add predictive capacity to strong content-and- rate only baselines. Additional experiments on domain transfer suggest that conversation- structure features often generalize to other communities better than conversation-content features do.

Subfields: Artificial Intelligence, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “For example, Hessel & Lee (2019); Larionov et al. (2018) train a “controversiality” classiﬁer based on Reddit’s controversiality scores (i.e. posts that have received both many upvotes and many down votes).”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “…safety sensitive situations In addition to the safety considerations outlined in §2, there are a number of potentially sensitive or controversial topics that may be unsuitable for a system to engage with, such as politics or religion (Hessel and Lee, 2019; Larionov et al., 2018; Xu et al., 2020).”
Does My Multimodal Model Learn Cross-modal Interactions? It’s Harder to Tell than You Might Think! (2020, unknown): “…al. (2019) datasets; more detail in Appendix E. 11Feature extraction approaches are known to produce competitive results relative to full fine-tuning (Devlin et al., 2019, §5.3); in some cases, mean pooling has been found to be similarly competitive relative to LSTM pooling (Hessel and Lee, 2019).”