Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features
This work addresses the challenge of early detection of controversial content for online community moderators, though it is incremental in nature.
The paper tackles the problem of predicting whether online posts will become controversial by using features from early comments and content, achieving improved predictive capacity even with limited initial data.
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.