# Tackling Online Abuse: A Survey of Automated Abuse Detection Methods

arXiv: https://arxiv.org/abs/1908.06024 · HTML: https://mlsanity.com/paper/1908.06024/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 30 of 33)

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- [MTTM: Metamorphic Testing for Textual Content Moderation Software](https://mlsanity.com/paper/2302.05706/) (2023)
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- [Emotion Based Hate Speech Detection using Multimodal Learning](https://mlsanity.com/paper/2202.06218/) (2022)
- [Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology](https://mlsanity.com/paper/2405.06346/) (2024)
- [HateCheckHIn: Evaluating Hindi Hate Speech Detection Models](https://mlsanity.com/paper/2205.00328/) (2022)
- [Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations](https://mlsanity.com/paper/2503.01623/) (2025)
- [Explainable Abuse Detection as Intent Classification and Slot Filling](https://mlsanity.com/paper/2210.02659/) (2022)
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- [Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection](https://mlsanity.com/paper/2203.14349/) (2022)
- [Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster](https://mlsanity.com/paper/2406.04106/) (2024)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations](https://mlsanity.com/paper/2201.06384/) (2022)
- [Validating Multimedia Content Moderation Software via Semantic Fusion](https://mlsanity.com/paper/2305.13623/) (2023)
- [Rationale-Guided Few-Shot Classification to Detect Abusive Language](https://mlsanity.com/paper/2211.17046/) (2022)
- [A Dataset for the Detection of Dehumanizing Language](https://mlsanity.com/paper/2402.08764/) (2024)
- [Trigger Warnings: Bootstrapping a Violence Detector for FanFiction](https://mlsanity.com/paper/2209.04409/) (2022)
- [An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software](https://mlsanity.com/paper/2308.09810/) (2023)
- [A Survey of Online Hate Speech through the Causal Lens](https://mlsanity.com/paper/2109.08120/) (2021)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [Politeness Stereotypes and Attack Vectors: Gender Stereotypes in Japanese and Korean Language Models](https://mlsanity.com/paper/2306.09752/) (2023)
- [DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures](https://mlsanity.com/paper/2307.16811/) (2023)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [A multitask learning framework for leveraging subjectivity of annotators to identify misogyny](https://mlsanity.com/paper/2406.15869/) (2024)
- [Detoxify: A framework for abusive text transformation using LLMs](https://mlsanity.com/paper/2507.10177/) (2025)
- [Metamorphic Testing for Audio Content Moderation Software](https://mlsanity.com/paper/2509.24215/) (2025)

## Abstract (arXiv)

Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abuse detection in the field of natural language processing (NLP). In this paper, we present a comprehensive survey of the methods that have been proposed to date, thus providing a platform for further development of this area. We describe the existing datasets and review the computational approaches to abuse detection, analyzing their strengths and limitations. We discuss the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability