Automatic Detection of Online Jihadist Hate Speech
Tom De Smedt, Guy De Pauw, Pieter Van Ostaeyen
TL;DR
This paper presents an automated system that detects online jihadist hate speech with over 80% accuracy, utilizing NLP and machine learning on a large Twitter dataset, with analysis of rhetoric and user networks.
Contribution
The paper introduces a novel automated detection system for jihadist hate speech, combining NLP, machine learning, and network analysis on Twitter data.
Findings
Achieved over 80% detection accuracy.
Analyzed jihadist rhetoric and Twitter user networks.
Provided technical details of the detection system.
Abstract
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.
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Terrorism, Counterterrorism, and Political Violence · Spam and Phishing Detection