# Retweet communities reveal the main sources of hate speech

arXiv: https://arxiv.org/abs/2105.14898 · HTML: https://mlsanity.com/paper/2105.14898/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [Echo Chambers: Emotional Contagion and Group Polarization on Facebook](https://mlsanity.com/paper/1607.01032/) (2016)
- [Multilingual Twitter Sentiment Classification: The Role of Human Annotators](https://mlsanity.com/paper/1602.07563/) (2016)
- [The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English](https://mlsanity.com/paper/1906.02045/) (2019)
- [How to evaluate sentiment classifiers for Twitter time-ordered data?](https://mlsanity.com/paper/1803.05160/) (2018)
- [Sustained Online Amplification of COVID-19 Elites in the United States](https://mlsanity.com/paper/2009.07255/) (2020)
- [The Limited Reach of Fake News on Twitter during 2019 European Elections](https://mlsanity.com/paper/1911.12039/) (2019)
- [Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities](https://mlsanity.com/paper/1608.04917/) (2016)
- [Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic](https://mlsanity.com/paper/2011.06845/) (2020)
- [Community evolution in retweet networks](https://mlsanity.com/paper/2105.06214/) (2021)

## Built on by (most cited first, 5 of 5)

- [Convolutional Learning on Multigraphs](https://mlsanity.com/paper/2209.11354/) (2022)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Code-mixed Sentiment and Hate-speech Prediction](https://mlsanity.com/paper/2405.12929/) (2024)
- [Analyzing User Characteristics of Hate Speech Spreaders on Social Media](https://mlsanity.com/paper/2310.15772/) (2023)
- [IMMENSE: Inductive Multi-perspective User Classification in Social Networks](https://mlsanity.com/paper/2608.05259/) (2026)

## Abstract (arXiv)

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.