通过用户社交上下文检测网络仇恨者，提升识别精准度。
Social Hatred: Efficient Multimodal Detection of Hatemongers
- 融合文本、行为与社交网络数据，从用户层面识别仇恨传播者。
- 在推特、Gab、Parler三平台验证，显著优于纯文本或图方法。
- 适合平台治理、内容审核及仇恨言论干预研究者使用。
自动检测网络仇恨言论是净化网络话语的关键步骤，准确分类有助于理解仇恨的扩散机制。现有研究多聚焦于语句级仇恨内容识别，我们提出关注用户层级的检测同样重要且具挑战性。本文提出一种多模态聚合方法，综合考虑用户的潜在仇恨文本、行为模式及其社交网络关系。在三个独立数据集（推特、Gab、Parler）上评估，结果显示结合用户社交上下文处理其文本，显著提升仇恨传播者识别效果，优于以往仅依赖文本或图结构的方法。实验提供多种设置下的完整结果，并包含典型案例的定性分析。该方法可有效识别隐晦表达、暗语式煽动及种族性误导等复杂形式，支持平台干预策略制定。此外，多模态方法在不同内容平台和大规模网络中均表现稳健。
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. Evaluating our method on three unique datasets X (Twitter), Gab, and Parler we show that 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. We offer comprehensive set of results obtained in different experimental settings as well as qualitative analysis of illustrative cases. Our method can be used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as to inform intervention measures. Moreover, we demonstrate that our multimodal approach performs well across very different content platforms and over large datasets and networks.
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