  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.

Social and Information Networks;Computers and Society;Applications;Machine Learning