Thou shall not hate：Countering Online Hate Speech（英语：Thou shall not hate：Countering Online Hate Speech）
Thou shalt not hate: Countering Online Hate Speech
摘要：
社交媒体上的仇恨内容越来越多。虽然Facebook、Twitter和谷歌试图采取几项措施来解决仇恨内容，但大多数都没有成功。反言论被认为是一种有效的方式来解决网络仇恨，而不会损害言论自由。因此，这些平台的另一种策略可能是促进反言论，作为对仇恨内容的防御。然而，为了成功地推广这种反言论，人们必须深入了解其在网络世界中的动态。缺乏精心策划的数据在很大程度上阻碍了这种理解。在本文中，我们使用YouTube上的评论创建并发布了有史以来第一个反言论数据集。该数据包含13，924条手动注释的评论，其中标签指示评论是否是反话。这些数据使我们能够第一次进行严格的测量研究，以表征反言语的语言结构。这种分析产生了各种有趣的见解，例如：与非反言论评论相比，反言论评论收到更多的喜欢，对于某些社区，大多数非反言论评论往往是仇恨言论，不同类型的反驳并不都是同样有效的，并且发布反驳的用户的语言选择与发布非反驳的用户的语言选择有很大不同。通过详细的心理语言学分析揭示的反言语。最后，我们构建了一组机器学习模型，这些模型能够自动检测YouTube视频中的反语音，F1得分为0.71。我们还构建了多标签模型，可以检测F1评分为0.60的评论中不同类型的反驳言论。
英文摘要：
Hate content in social media is ever-increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle the hateful content, they have mostly been unsuccessful. Counterspeech is seen as an effective way of tackling the online hate without any harm to the freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 13,924 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive much more likes as compared to the non-counterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting non-counterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.71. We also build multilabel models that can detect different types of counterspeech in a comment with an F1-score of 0.60.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
