Towards a comprehensive taxonomy of online abusive language informed by machine leaning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 83 references that are in the map. First posted on arXiv 24 Apr 2025.
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References · 18
- 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 407
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- 2021Towards generalisable hate speech detection: a review on obstacles and solutionscited by 66
6 more
- 2021Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hatecited by 50
- 2021ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AIcited by 46
- 2023Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarkscited by 46
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- 2020Detecting East Asian Prejudice on Social Mediacited by 40
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- 2025A survey of textual cyber abuse detection using cutting-edge language models and large language models
- 2025Context Matters: Incorporating Target Awareness in Conversational Abusive Language Detection
- 2024Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators
- 2021Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
- 2024Analyzing Toxicity in Deep Conversations: A Reddit Case Study
- 2025Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior
- 2022A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
- 2023How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments