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Online antisocial behavior, such as cyberbullying, harassment, and trolling,is a widespread problem that threatens free discussion and has negativephysical and mental health consequences for victims and communities. Whileprior work has proposed automated methods to identify hostile comments inonline discussions, these methods work retrospectively on comments that havealready been posted, making it difficult to intervene before an interactionescalates. In this paper we instead consider the problem of forecasting futurehostilities in online discussions, which we decompose into two tasks: (1) givenan initial sequence of non-hostile comments in a discussion, predict whethersome future comment will contain hostility; and (2) given the first hostilecomment in a discussion, predict whether this will lead to an escalation ofhostility in subsequent comments. Thus, we aim to forecast both the presenceand intensity of hostile comments based on linguistic and social features fromearlier comments. To evaluate our approach, we introduce a corpus of over 30Kannotated Instagram comments from over 1,100 posts. Our approach is able topredict the appearance of a hostile comment on an Instagram post ten or morehours in the future with an AUC of .82 (task 1), and can furthermoredistinguish between high and low levels of future hostility with an AUC of .91(task 2).

Forecasting the presence and intensity of hostility on Instagram using linguistic and social features