Measuring and Characterizing Hate Speech on News Websites
Savvas Zannettou, Mai ElSherief, Elizabeth Belding, Shirin Nilizadeh,, Gianluca Stringhini

TL;DR
This study analyzes 125 million comments on news articles over 19 months to understand what attracts hate speech, revealing correlations with divisive events, linguistic features, and cross-platform posting behavior.
Contribution
It provides a large-scale quantitative analysis of hate speech on news websites, linking hateful comments to specific events, linguistic traits, and social media activity, which was previously underexplored.
Findings
Hateful comments increase around divisive real-world events.
Articles with hateful comments have distinct linguistic features.
Posting news on /pol/ or certain subreddits correlates with increased hate speech.
Abstract
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…
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