Automatic Detection of Online Jihadist Hate Speech
Not on the map. Why?
Abstract (arXiv)
We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1.
4 papers.
- 2021Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Modelcited by 26
- 2021Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and Englishcited by 5
- 2023Toxic language detection: a systematic review of Arabic datasetscited by 3
- 2024Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detectioncited by 3
- 2024Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection16 Dec 2024
- 2023Toxic language detection: a systematic review of Arabic datasets12 Dec 2023
- 2021Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model2 Nov 2021
- 2021Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English3 Apr 2021