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.
@inproceedings{hessel-lee-2019-somethings, title = "Something{'}s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features", author = "Hessel, Jack and Lee, Lillian", booktitle = "Proceedings of NAACL, Volume 1 (Long and Short Papers)", year = "2019", url = "https://www.aclweb.org/anthology/N19-1166", pages = "1648--1659", abstract = "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.", }
This material is based upon work supported by the National Science Foundation under Grant No. SES-1741441. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views or official policies, either expressed or implied, of any sponsoring institutions, the U.S. government, or any other entity.
