# The Effect of Extremist Violence on Hateful Speech Online

arXiv: https://arxiv.org/abs/1804.05704 · HTML: https://mlsanity.com/paper/1804.05704/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://mlsanity.com/paper/2204.12543/) (2022)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [ToXCL: A Unified Framework for Toxic Speech Detection and Explanation](https://mlsanity.com/paper/2403.16685/) (2024)
- [Leveraging cross-platform data to improve automated hate speech detection](https://mlsanity.com/paper/2102.04895/) (2021)
- [A Survey of Online Hate Speech through the Causal Lens](https://mlsanity.com/paper/2109.08120/) (2021)
- [Towards Weakly-Supervised Hate Speech Classification Across Datasets](https://mlsanity.com/paper/2305.02637/) (2023)
- [LaMSUM: Amplifying Voices Against Harassment through LLM Guided Extractive Summarization of User Incident Reports](https://mlsanity.com/paper/2406.15809/) (2024)
- [Culture Matters in Toxic Language Detection in Persian](https://mlsanity.com/paper/2506.03458/) (2025)
- [Reproducibility Report: Contextualizing Hate Speech Classifiers with Post-hoc Explanation](https://mlsanity.com/paper/2105.11412/) (2021)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [Incorporating Human Explanations for Robust Hate Speech Detection](https://mlsanity.com/paper/2411.06213/) (2024)
- [Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responses](https://mlsanity.com/paper/2508.15855/) (2025)
- [Triggered: A Statistical Analysis of Environmental Influences on Extremist Groups](https://mlsanity.com/paper/2602.09289/) (2026)
- [Simulating Hate Speech Cascades with Multi-LLM Agents: Empirical Grounding, Modeling Fidelity, and Intervention Strategies](https://mlsanity.com/paper/2606.18264/) (2026)
- [Unifying Models of Intergroup Hostility in Online Discourse](https://mlsanity.com/paper/2609.20808/) (2026)

## Abstract (arXiv)

User-generated content online is shaped by many factors, including endogenous elements such as platform affordances and norms, as well as exogenous elements, in particular significant events. These impact what users say, how they say it, and when they say it. In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence. We anchor this study in a series of attacks involving Arabs and Muslims as perpetrators or victims, occurring in Western countries, that have been covered extensively by news media. These attacks have fueled intense policy debates around immigration in various fora, including online media, which have been marred by racist prejudice and hateful speech. The focus of our research is to model the effect of the attacks on the volume and type of hateful speech on two social media platforms, Twitter and Reddit. Among other findings, we observe that extremist violence tends to lead to an increase in online hate speech, particularly on messages directly advocating violence. Our research has implications for the way in which hate speech online is monitored and suggests ways in which it could be fought.