YouTube based religious hate speech and extremism detection dataset with machine learning baselines

Issue title: Recent Advances in Language & Knowledge Engineering

Guest editors: David Pinto, Beatriz Beltrán and Vivek Singh

Article type: Research Article

Authors: Ashraf, Noman^{a; *} | Rafiq, Abid^b | Butt, Sabur^a | Shehzad, Hafiz Muhammad Faisal^b | Sidorov, Grigori^a | Gelbukh, Alexander^a

Affiliations: [a] CIC, Instituto PolitÃl’ cnico Nacional, Mexico | [b] Department of Computer Science and IT, University of Sargodha, Pakistan

Correspondence: [*] Corresponding author. Noman Ashraf, CIC, Instituto PolitÃl’cnico Nacional, Mexico. E-mail: [email protected].

Abstract: On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.

Keywords: Hate speech detection, religious extremism detection, YouTube comment analysis, hate speech dataset

DOI: 10.3233/JIFS-219264

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4769-4777, 2022