# Measuring and Characterizing Hate Speech on News Websites

arXiv: https://arxiv.org/abs/2005.07926 · HTML: https://mlsanity.com/paper/2005.07926/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 12 of 12)

- [You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content](https://mlsanity.com/paper/2308.05596/) (2023)
- [Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots](https://mlsanity.com/paper/2209.03463/) (2022)
- [VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform](https://mlsanity.com/paper/2504.14904/) (2025)
- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning](https://mlsanity.com/paper/2209.09803/) (2022)
- [Understanding Online Migration Decisions Following the Banning of Radical Communities](https://mlsanity.com/paper/2212.04765/) (2022)
- [ToxBuster: In-game Chat Toxicity Buster with BERT](https://mlsanity.com/paper/2305.12542/) (2023)
- [Towards Detecting Contextual Real-Time Toxicity for In-Game Chat](https://mlsanity.com/paper/2310.18330/) (2023)
- [Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube](https://mlsanity.com/paper/2103.09050/) (2021)
- [How toxic is antisemitism? Potentials and limitations of automated toxicity scoring for antisemitic online content](https://mlsanity.com/paper/2310.04465/) (2023)
- [Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platforms](https://mlsanity.com/paper/2602.03520/) (2026)
- [Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment](https://mlsanity.com/paper/2606.02946/) (2026)

## Abstract (arXiv)

The Web has become the main source for news acquisition. At the same time, news discussion has become more social: users can post comments on news articles or discuss news articles on other platforms like Reddit. These features empower and enable discussions among the users; however, they also act as the medium for the dissemination of toxic discourse and hate speech. The research community lacks a general understanding on what type of content attracts hateful discourse and the possible effects of social networks on the commenting activity on news articles. In this work, we perform a large-scale quantitative analysis of 125M comments posted on 412K news articles over the course of 19 months. We analyze the content of the collected articles and their comments using temporal analysis, user-based analysis, and linguistic analysis, to shed light on what elements attract hateful comments on news articles. We also investigate commenting activity when an article is posted on either 4chan's Politically Incorrect board (/pol/) or six selected subreddits. We find statistically significant increases in hateful commenting activity around real-world divisive events like the "Unite the Right" rally in Charlottesville and political events like the second and third 2016 US presidential debates. Also, we find that articles that attract a substantial number of hateful comments have different linguistic characteristics when compared to articles that do not attract hateful comments. Furthermore, we observe that the post of a news articles on either /pol/ or the six subreddits is correlated with an increase of (hateful) commenting activity on the news articles.