TY - JOUR
T1 - YouTube based religious hate speech and extremism detection dataset with machine learning baselines
AU - Ashraf, Noman
AU - Rafiq, Abid
AU - Butt, Sabur
AU - Shehzad, Hafiz Muhammad Faisal
AU - Sidorov, Grigori
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2022 - IOS Press. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Hate speech detection
KW - YouTube comment analysis
KW - hate speech dataset
KW - religious extremism detection
UR - http://www.scopus.com/inward/record.url?scp=85128185741&partnerID=8YFLogxK
U2 - 10.3233/JIFS-219264
DO - 10.3233/JIFS-219264
M3 - Artículo
AN - SCOPUS:85128185741
SN - 1064-1246
VL - 42
SP - 4769
EP - 4777
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 5
ER -