SICYAPMLApr 2, 2015

Antisocial Behavior in Online Discussion Communities

arXiv:1504.00680v2342 citations
AI Analysis

This addresses the challenge of maintaining healthy online communities for moderators and users, but it is incremental as it builds on existing research with new data and insights.

The paper tackled the problem of antisocial behavior in online discussion communities by analyzing banned users, finding that they concentrate efforts in few threads, post irrelevantly, and garner more responses, with behavior worsening over time and exacerbated by harsh feedback.

User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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