Right-wing German Hate Speech on Twitter: Analysis and Automatic Detection
Not on the map. Why?
Abstract (arXiv)
Discussion about the social network Twitter often concerns its role in political discourse, involving the question of when an expression of opinion becomes offensive, immoral, and/or illegal, and how to deal with it. Given the growing amount of offensive communication on the internet, there is a demand for new technology that can automatically detect hate speech, to assist content moderation by humans. This comes with new challenges, such as defining exactly what is free speech and what is illegal in a specific country, and knowing exactly what the linguistic characteristics of hate speech are. To shed light on the German situation, we analyzed over 50,000 right-wing German hate tweets posted between August 2017 and April 2018, at the time of the 2017 German federal elections, using both quantitative and qualitative methods. In this paper, we discuss the results of the analysis and demonstrate how the insights can be employed for the development of automatic detection systems.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 2; 2025: 2; 2026: 1.
9 papers.
- 2021A systematic review of Hate Speech automatic detection using Natural Language Processingcited by 26
- 2021Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Modelcited by 26
- 2022Human-in-the-Loop Hate Speech Classification in a Multilingual Contextcited by 3
- 2024A Survey on Automatic Online Hate Speech Detection in Low-Resource Languagescited by 2
- 2025Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processingcited by 2
- 2022The use of the word "gammaupsionnuαiotakappaomicronkappaτomicronnuiotaα" (femicide) in Greek-speaking Twittercited by 0
- 2024Hate Speech Detection and Classification in Amharic Text with Deep Learningcited by 0
- 2025The "Right" Discourse on Migration: Analysing Migration-Related Tweets in Right and Far-Right Political Movementscited by 0
- 2026From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigationcited by 0
- 2026From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation26 Aug 2026
- 2025The "Right" Discourse on Migration: Analysing Migration-Related Tweets in Right and Far-Right Political Movements24 Oct 2025
- 2025Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing7 Mar 2025
- 2024A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages28 Nov 2024
- 2024Hate Speech Detection and Classification in Amharic Text with Deep Learning7 Aug 2024
- 2022Human-in-the-Loop Hate Speech Classification in a Multilingual Context5 Dec 2022
- 2022The use of the word "gammaupsionnuαiotakappaomicronkappaτomicronnuiotaα" (femicide) in Greek-speaking Twitter21 Oct 2022
- 2021Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model2 Nov 2021
- 2021A systematic review of Hate Speech automatic detection using Natural Language Processing22 May 2021