Retweet communities reveal the main sources of hate speech

Abstract

We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. In fact, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the years 2018-2020.

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Cited by in corpus (6)

• Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art
• The Systemic Impact of Deplatforming on Social Media
• Community evolution in retweet networks
• Convolutional Learning on Multigraphs
• Bow-Tie Structures of Twitter Discursive Communities
• Code-mixed Sentiment and Hate-speech Prediction