Paper: Antisocial Behavior in Online Discussion Communities
Summary:
Although antisocial behavior in online communities is very common, most of the recent research on this subject has focused on qualitative analysis using a small group of users. This paper uses data from three online communities (CNN.com, breitbart.com, and IGN.com) for quantitative analysis to get a general understanding of the antisocial behavior of users. For the sake of comparison, the paper discusses two types of users: Future-Banned-Users (FBU) and Never-Banned-Users (NBU). By analyzing the post throughout their activity span on the forum authors find that posts by the FBUs are very different than other posts in the thread and harder to read. They also find that their quality of posts worsens over time. Censorship also plays a role in the guiding the writing style of the FBUs. FBUs post a lot and get a higher number of responses. Another finding of the study is that two types of FBUs exist in an online community: one with higher post deletion rate and other with lower. Finally, the authors use four types of features to create a classifier for predicting antisocial behavior: post feature, activity feature, community feature and moderator feature.
Reflection:
The paper explains the process of analyzing antisocial behavior starting from data preparation to analysis in great detail. One interesting aspect of the process was using crowdsourcing for initial classification. The authors’ analysis of the final classifier provided some interesting insight. For example, classifiers performance peaks on seeing the attributes from first 10 posts. This correlates with the idea that other community members judge FBUs in a similar fashion. The performance of the classifier on Hi-FBUs suggests that the classifier learns the post deletion ratio as one of the primary indicators which explains why prediction performance peaks at seeing first 10 posts. The authors’ analysis of the cross-platform performance of the algorithm was very intuitive. Although the prediction quality of the classifier is good enough, there remains the issue of application of such tools. Finally, this paper discusses a sensitive issue of antisocial behavior and creates a tool for prediction. Although the performance is good enough, still there is a necessity of the human factor in preventing such behavior.
