构建在线辱骂语言的分级分类体系，助力精准识别与治理。
Towards a comprehensive taxonomy of online abusive language informed by machine leaning
- 整合18个多标签数据集，系统提炼辱骂语言核心特征。
- 形成5类17维的层级化分类框架，涵盖目标、强度、主题等维度。
- 为研究者、平台方和政策制定者提供统一分析工具。
在线交流中辱骂语言的泛滥对个人与社群健康构成重大威胁。为应对这一问题，亟需有效识别与缓解有害内容，并实现持续监控与早期干预。本文提出一种系统化方法，基于18个现有多标签数据集的分类体系，构建在线辱骂语言的分类框架。该框架为层级化与多维结构，包含5个类别与17个维度，可刻画辱骂行为的语境、目标、强度、直接性及主题等特征。此统一认知有助于推动研究者、政策制定者、平台运营方等多方协作，促进在线滥用内容检测与治理领域的知识共享与技术进步。
原文摘要 · Abstract (English)
The proliferation of abusive language in online communications has posed significant risks to the health and wellbeing of individuals and communities. The growing concern regarding online abuse and its consequences necessitates methods for identifying and mitigating harmful content and facilitating continuous monitoring, moderation, and early intervention. This paper presents a taxonomy for distinguishing key characteristics of abusive language within online text. Our approach uses a systematic method for taxonomy development, integrating classification systems of 18 existing multi-label datasets to capture key characteristics relevant to online abusive language classification. The resulting taxonomy is hierarchical and faceted, comprising 5 categories and 17 dimensions. It classifies various facets of online abuse, including context, target, intensity, directness, and theme of abuse. This shared understanding can lead to more cohesive efforts, facilitate knowledge exchange, and accelerate progress in the field of online abuse detection and mitigation among researchers, policy makers, online platform owners, and other stakeholders.
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