2019/04/15 by Jack Hessel, Lillian Lee, Hessel, Jack +1 · 4 citations
Computer Science · Physics and Astronomy · Social Sciences · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Physical sciences #Misinformation and Its Impacts #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Topic Modeling #cs.CL #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1904.07372
Accepted at NAACL 2019 as a long paper
arxiv created 2019/04/15 · openalex publication_date 2019/04/15 · arxiv updated 2019/04/17 · openalex created_date 2022/07/29 · openalex updated_date 2026/08/04
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.