通过分析论证结构预测仇恨内容，准确率最高达96%
Leveraging Argument Structure to Predict Content Hatefulness
- 利用论证成分的可信度与仇恨标记构建整体内容预测模型
- 在WSF-ARG+数据集上达到96% F1值，表现优异
- 适合研究仇恨言论检测与信息失序对抗的学者参考
信息失序是影响社会的严峻现象，表现为误导性、错误信息和仇恨内容的传播。不同情境下问题侧重点不同，但整体需综合应对。本文探索论证结构在连接仇恨言论、虚假信息等不同维度中的作用。聚焦于包含白人至上论坛消息的WSF-ARG+数据集，该数据集对论证结构（前提与结论）进行了标注。通过利用论证组件的可信度与仇恨标记，我们获得了对整条消息仇恨程度的洞察。结果表明，该方法展现出良好前景（最高96% F1），未来有望扩展至仇恨内容识别与信息失序防控。
原文摘要 · Abstract (English)
Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and hateful content online. In different contexts, one aspect of the problem may prevail, but overall, this is a broad problem that requires comprehensive solutions. While each dimension of the problem (hate speech, disinformation, misinformation, etc.) requires in-depth analysis, in this paper, we look into the possibility of argument structure to provide relevant information to link these different areas of the problem. In particular, we focus on the WSF-ARG+ dataset, which consists of white supremacy forum messages annotated in terms of argument structure (premises and conclusion). There, we leverage the checkworthiness and hatefulness annotations of the argument components to obtain insights into the hatefulness of the whole message. Our results show promising insights (up to 96% F1), indicating the possibility of extending this direction in the future to tackle hateful content identification and information disorder countering.
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