  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 to
human moderators, a system needs to preemptively detect that a conversation is
heading towards derailment before it actually turns toxic. This means modeling
derailment as an emerging property of a conversation rather than as an isolated
utterance-level event.
  Forecasting emerging conversational properties, however, poses several
inherent modeling challenges. First, since conversations are dynamic, a
forecasting model needs to capture the flow of the discussion, rather than
properties of individual comments. Second, real conversations have an unknown
horizon: they can end or derail at any time; thus a practical forecasting model
needs to assess the risk in an online fashion, as the conversation develops. In
this work we introduce a conversational forecasting model that learns an
unsupervised representation of conversational dynamics and exploits it to
predict future derailment as the conversation develops. By applying this model
to two new diverse datasets of online conversations with labels for antisocial
events, we show that it outperforms state-of-the-art systems at forecasting
derailment.
