Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

Abraham Israeli, Oren Tsur
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引用次数: 6

Abstract

Social platforms such as Gab and Parler, branded as ‘free-speech’ networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit.In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make 16.1% of Parler active users, and that they have distinct characteristics comparing to other user groups. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.
言论自由还是仇恨言论自由?分析律师中仇恨言论的扩散
Gab和Parler等社交平台被标榜为“言论自由”的网络,近年来用户基础显著增长。这种受欢迎程度主要归因于Twitter、Facebook和Reddit等主流平台实施的更严格的审核。在这项工作中,我们首次对Parler上的仇恨言论进行了大规模分析。我们对仇恨言论检测的一系列算法进行了实验,证明了迁移学习在该领域的局限性,因为仇恨言论的传递方式具有欺骗性和不断变化的性质。为了提高分类精度,我们注释了10K个Parler帖子,我们用它来微调BERT分类器。然后,通过社交网络上的标签传播,对单个帖子进行分类,从而对数百万用户进行分类。根据传播仇恨的倾向对用户进行分类,我们发现仇恨贩子占Parler活跃用户的16.1%,与其他用户群体相比,他们具有明显的特征。通过比较在Parler中观察到的趋势与在Gab中发现的趋势,我们进一步补充了我们的分析。据我们所知,这是第一批以定量方式和用户层面分析Parler中的仇恨言论的作品之一。
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