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Online discussions often derail into toxic exchanges between participants.Recent efforts mostly focused on detecting antisocial behavior after the fact,by analyzing single comments in isolation. To provide more timely notice tohuman moderators, a system needs to preemptively detect that a conversation isheading towards derailment before it actually turns toxic. This means modelingderailment as an emerging property of a conversation rather than as an isolatedutterance-level event.  Forecasting emerging conversational properties, however, poses severalinherent modeling challenges. First, since conversations are dynamic, aforecasting model needs to capture the flow of the discussion, rather thanproperties of individual comments. Second, real conversations have an unknownhorizon: they can end or derail at any time; thus a practical forecasting modelneeds to assess the risk in an online fashion, as the conversation develops. Inthis work we introduce a conversational forecasting model that learns anunsupervised representation of conversational dynamics and exploits it topredict future derailment as the conversation develops. By applying this modelto two new diverse datasets of online conversations with labels for antisocialevents, we show that it outperforms state-of-the-art systems at forecastingderailment.

Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop