地平线上的麻烦：随着在线对话的发展，预测其出轨
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop
摘要：
在线讨论往往会导致参与者之间的有害交流。最近的努力主要集中在事后检测反社会行为，通过孤立地分析单个评论。为了向人类版主提供更及时的通知，系统需要在对话实际上变得有毒之前先发制人地检测到对话正在脱轨。这意味着将出轨建模为对话的新兴属性，而不是孤立的话语级事件。然而，预测新兴的会话属性，提出了一些固有的建模挑战。首先，由于对话是动态的，预测模型需要捕捉讨论的流程，而不是单个评论的属性。其次，真实的对话具有未知的范围：它们可以随时结束或脱轨;因此，随着对话的发展，实用的预测模型需要以在线方式评估风险。在这项工作中，我们引入了一个会话预测模型，学习会话动态的无监督表示，并利用它来预测未来的出轨会话的发展。通过将该模型应用于两个新的不同的带有反社会事件标签的在线对话数据集，我们表明它在预测出轨方面优于最先进的系统。
英文摘要：
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.
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