论文标题
使用情感信息在线对话中先发制于有毒评论
Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations
论文作者
论文摘要
最近，自动检测有毒评论的挑战是最近进行了大量研究的主题，但是重点是在发布后主要是在单个消息中检测到它。一些作者试图通过前几条消息的功能预测对话是否会脱离毒性。在本文中，我们将这种方法与使用情感信息有关毒性检测的先前工作结合在一起，并展示在对话的第一条消息中表达的情感如何有助于预测即将到来的毒性。我们的结果表明，添加情感功能确实有助于提高毒性预测的准确性，还使我们能够对先发制体毒性检测的一般任务进行重要的观察。
The challenge of automatic detection of toxic comments online has been the subject of a lot of research recently, but the focus has been mostly on detecting it in individual messages after they have been posted. Some authors have tried to predict if a conversation will derail into toxicity using the features of the first few messages. In this paper, we combine that approach with previous work on toxicity detection using sentiment information, and show how the sentiments expressed in the first messages of a conversation can help predict upcoming toxicity. Our results show that adding sentiment features does help improve the accuracy of toxicity prediction, and also allow us to make important observations on the general task of preemptive toxicity detection.
