One of the main challenges online social systems face is the prevalence of
antisocial behavior, such as harassment and personal attacks. In this work, we
introduce the task of predicting from the very start of a conversation whether
it will get out of hand. As opposed to detecting undesirable behavior after the
fact, this task aims to enable early, actionable prediction at a time when the
conversation might still be salvaged.
To this end, we develop a framework for capturing pragmatic devices---such as
politeness strategies and rhetorical prompts---used to start a conversation,
and analyze their relation to its future trajectory. Applying this framework in
a controlled setting, we demonstrate the feasibility of detecting early warning
signs of antisocial behavior in online discussions.

本研究旨在提出一种初始预测在线讨论是否会演变成骚扰或人身攻击的框架，该框架可以捕捉与未来在线讨论轨迹相关的优雅用语等语言特征，并在受控环境中展示了检测在线讨论的早期预警迹象的可行性。