Since traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate users content. One popular platform relevant for the German hater community is Telegram for which limited research efforts have been made so far. This study aims to develop a broad framework comprising (i) an abusive language classification model for German Telegram messages and (ii) a classification model for the hatefulness of Telegram channels. For the first part, we use existing abusive language datasets containing posts from other platforms to develop our classification models. For the channel classification model, we develop a method that combines channel-specific content information collected from a topic model with a social graph to predict the hatefulness of channels. Furthermore, we complement these two approaches for hate speech detection with insightful results on the evolution of the hater community on Telegram in Germany. We also propose methods for conducting scalable network analyses for social media platforms to the hate speech research community. As an additional output of this study, we provide an annotated abusive language dataset containing 1,149 annotated Telegram messages.
翻译:由于传统的社交媒体平台继续禁止传播仇恨言论或其他形式虐待性语言的行为者(一个称为变形过程),这些行为者迁移到不温和用户内容的替代平台。一个与德国仇恨者社区有关的流行平台是Telegram,迄今为止对此的研究努力有限。这项研究旨在开发一个广泛的框架,包括(一) 德国电信信息滥用语言分类模式和(二) Telegram 频道仇恨性的分类模式。首先,我们使用含有其他平台文章的现有虐待性语言数据集来开发我们的分类模式。关于频道分类模式,我们开发了一种方法,将从一个主题模型收集的频道特定内容信息与社会图表相结合,以预测各频道的仇恨性。此外,我们用德国Telegram 网站仇恨者社区演变的深刻结果来补充这两个识别仇恨言论的方法。我们还提出了为仇恨言论研究社区进行可扩缩网络分析的方法。作为本研究的一项附加产出,我们提供了带有1,149个附加注释的语音图文。