Abstract
Nowadays, social networks have become powerful mediums of communication providing information, learning and entertainment. Unfortunately, these platforms can be sorely manipulated by vicious users sharing malicious contents. Therefore, the process of mining and analyzing such published suspicious content is a considerably challenging task that serves to fight against the online radicalization. For this purpose, we propose, in this paper, a new methodology for extracting and analyzing violent vocabulary shared on social networks with the exploration a set of natural language processing and data mining techniques. Our method relies mainly on extracting a set of profiles judged by a domain expert as extremist and non-extremist’ users. Then, we focus on their shared textual content in order to detect malicious vocabulary published within the radical context as well as their violence’ degrees. Finally, in order to evaluate the performance of our method, we resort to an expert who verifies the final list of the extracted vocabulary annotated by our method. Thus, the given results show its effectiveness as well as its efficiency.
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This publication was made possible by NPRP grant #9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
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Rekik, A., Jamoussi, S., Hamadou, A.B. (2019). Violent Vocabulary Extraction Methodology: Application to the Radicalism Detection on Social Media. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_9
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DOI: https://doi.org/10.1007/978-3-030-28374-2_9
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