User-generated content is critical to the success of any online platforms. While most users tend to be civil, others may engage in antisocial behavior. Such undesired behavior includes trolling, flaming, bullying, and harassment. Despite its severity and prevalence, surprisingly little is known about online antisocial behavior. This can lead to new methods for identifying undesirable users and minimizing troll-like behavior, which can ultimately result in healthier online communities.
Researchers looked at three large online discussion-based communities. In these communities, members that repeatedly violate community norms are eventually banned permanently. Such individuals are clear instances of antisocial users, and constitute “ground truth” in our analyses. We find that the behavior of an FBU worsens over their active tenure in a community. This suggests that communities may play a part in incubating antisocial behavior.
Antisocial users are less tolerant of such users the longer they remain in a community. This results in an increased rate at which their posts are deleted, even after controlling for post quality. We show that a user’s posting behavior can be used to make predictions about who will be banned in the future. We can predict with over 80% AUC (area under the ROC curve) whether a user will be subsequently banned.
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