# Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

arXiv: https://arxiv.org/abs/2104.10788 · HTML: https://mlsanity.com/paper/2104.10788/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2021.

## Built on by (most cited first, 29 of 29)

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- [Detecting Conversational Mental Manipulation with Intent-Aware Prompting](https://mlsanity.com/paper/2412.08414/) (2024)
- [Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities](https://mlsanity.com/paper/2406.07353/) (2024)
- [CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network](https://mlsanity.com/paper/2303.03387/) (2023)
- [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022)
- [Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes](https://mlsanity.com/paper/2411.12174/) (2024)
- [Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models](https://mlsanity.com/paper/2305.14307/) (2023)
- [DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship](https://mlsanity.com/paper/2411.01844/) (2024)
- [Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships](https://mlsanity.com/paper/2307.02763/) (2023)
- [Putting Context in Context: the Impact of Discussion Structure on Text Classification](https://mlsanity.com/paper/2402.02975/) (2024)
- [Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums](https://mlsanity.com/paper/2407.03551/) (2024)
- [Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities](https://mlsanity.com/paper/2408.09366/) (2024)
- [Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models](https://mlsanity.com/paper/2501.09039/) (2025)
- [Large Language Models Help Reveal Unhealthy Diet and Body Concerns in Online Eating Disorders Communities](https://mlsanity.com/paper/2401.09647/) (2024)
- [NLP Case Study on Predicting the Before and After of the Ukraine-Russia and Hamas-Israel Conflicts](https://mlsanity.com/paper/2410.06427/) (2024)
- [Exploring Large Language Models for Hate Speech Detection in Rioplatense Spanish](https://mlsanity.com/paper/2410.12174/) (2024)
- [Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon](https://mlsanity.com/paper/2505.22184/) (2025)
- [Digital Gatekeepers: Google's Role in Curating Hashtags and Subreddits](https://mlsanity.com/paper/2506.14370/) (2025)
- [SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection](https://mlsanity.com/paper/2511.11599/) (2025)
- [Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks](https://mlsanity.com/paper/2203.02123/) (2022)
- [Characterizing Online Toxicity During the 2022 Mpox Outbreak: A Computational Analysis of Topical and Network Dynamics](https://mlsanity.com/paper/2408.11962/) (2024)
- [Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata](https://mlsanity.com/paper/2504.01534/) (2025)
- [Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations](https://mlsanity.com/paper/2507.21089/) (2025)
- [Knowledge-guided Continual Learning for Behavioral Analytics Systems](https://mlsanity.com/paper/2510.22405/) (2025)
- [Text Detoxification in isiXhosa and Yor`ub'a: A Cross-Lingual Machine Learning Approach for Low-Resource African Languages](https://mlsanity.com/paper/2601.05624/) (2026)
- [Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts](https://mlsanity.com/paper/2603.01457/) (2026)
- [Harm is not Universal: Community-Specific Toxicity Detection is Urgently Needed](https://mlsanity.com/paper/2607.24898/) (2026)
- [Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM-Generated and Authentic Dialogues](https://mlsanity.com/paper/2609.17549/) (2026)

## Abstract (arXiv)

Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in disturbing toxic communication, such as cyberbullying and harassment. Nevertheless, detecting online toxicity is challenging due to its multi-dimensional, context sensitive nature. As exposure to online toxicity can have serious social consequences, reliable models and algorithms are required for detecting and analyzing such communication across the vast and growing space of social media. In this paper, we draw on psychological and social theory to define toxicity. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.