通过用户社交语境提升仇恨言论者识别准确率
AggregHate: An Efficient Aggregative Approach for the Detection of Hatemongers on Social Platforms
- 融合文本、用户行为与社交网络的多模态聚合方法
- 在三大平台数据上检测效果优于传统文本与图模型
- 高效处理大规模数据，适合打击隐晦仇恨表达
自动检测网络仇恨言论是净化网络话语的重要一步。现有研究多聚焦于内容层面的仇恨语句识别，而我们主张用户层面的检测同样关键且更具挑战性。本文提出一种多模态聚合方法，综合考虑潜在仇恨文本、用户行为及社交网络结构，对推特（Twitter）、Gab和Parler三个独特数据集进行评估。结果表明，在用户社交语境下处理其文本可显著提升仇恨煽动者的检测性能，优于以往的文本与图基方法。该方法还能有效识别隐晦表达如暗语、种族暗示性误导等，并支持干预策略制定。同时，本方法在大规模数据与网络中仍保持高效率。
原文摘要 · Abstract (English)
Automatic detection of online hate speech serves as a crucial step in the detoxification of the online discourse. Moreover, accurate classification can promote a better understanding of the proliferation of hate as a social phenomenon. While most prior work focus on the detection of hateful utterances, we argue that focusing on the user level is as important, albeit challenging. In this paper we consider a multimodal aggregative approach for the detection of hate-mongers, taking into account the potentially hateful texts, user activity, and the user network. We evaluate our methods on three unique datasets X (Twitter), Gab, and Parler showing that a processing a user's texts in her social context significantly improves the detection of hate mongers, compared to previously used text and graph-based methods. Our method can be then used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as inform intervention measures. Moreover, our approach is highly efficient even for very large datasets and networks.
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