A new multilingual radical-content dataset plus an analysis showing that annotation disagreement and socio-demographic factors shift model performance and bias metrics.
Automatic Detection of Online Jihadist Hate Speech
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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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Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection
A new multilingual radical-content dataset plus an analysis showing that annotation disagreement and socio-demographic factors shift model performance and bias metrics.