  Online antisocial behavior, such as cyberbullying, harassment, and trolling,
is a widespread problem that threatens free discussion and has negative
physical and mental health consequences for victims and communities. While
prior work has proposed automated methods to identify hostile comments in
online discussions, these methods work retrospectively on comments that have
already been posted, making it difficult to intervene before an interaction
escalates. In this paper we instead consider the problem of forecasting future
hostilities in online discussions, which we decompose into two tasks: (1) given
an initial sequence of non-hostile comments in a discussion, predict whether
some future comment will contain hostility; and (2) given the first hostile
comment in a discussion, predict whether this will lead to an escalation of
hostility in subsequent comments. Thus, we aim to forecast both the presence
and intensity of hostile comments based on linguistic and social features from
earlier comments. To evaluate our approach, we introduce a corpus of over 30K
annotated Instagram comments from over 1,100 posts. Our approach is able to
predict the appearance of a hostile comment on an Instagram post ten or more
hours in the future with an AUC of .82 (task 1), and can furthermore
distinguish between high and low levels of future hostility with an AUC of .91
(task 2).
