Abstract
The growth of the Internet in the last decade has created great opportunities for sharing content and opinions at a global scale. While this may look like a completely positive feature, it also facilitates the dissemination of discriminative material, propaganda calling for violence, etc. We present a system for recognition, classification and inspection of this kind of material in terms of different characteristics and identification of its authors. The system is illustrated using different sources – including Jihadist magazines and White Supremacist forum posts. We show experiments on the detection of offensive content, on its classification and provide a visualization and enrichment of extremist data.
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Available at https://www.kaggle.com/fifthtribe/isis-religious-texts.
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Soler-Company, J., Wanner, L. (2019). Automatic Classification and Linguistic Analysis of Extremist Online Material. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_49
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DOI: https://doi.org/10.1007/978-3-030-05716-9_49
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