Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier For Hate Speech Detection in Online Social Network
Canonical Correlation; Deep Belief Networks and Convolutional Neural Networks; Hate Speech Detection; Krill Herd Algorithm
Social networking uses internet-based platforms to facilitate users to make connections with others and share various forms of content, including text, images, videos, and links. Social networking services are mainly used for non-social interpersonal communication. Many approaches have been developed for hate speech detection, but they still face significant challenges, particularly in classifying text into multiple labels accurately and in a timely manner. For accurate hate speech detection in social networks, a Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier (CRCOCDBC) model is developed. The objective of the developed CRCOCDBC is to detect multi-class hate speech with minimal time and error rate. Comparative analysis shows improved performance in terms of minimum error and higher authentication accuracy and precision than other well-known methods. 2026 Seventh Sense Research Group.
Banu, I. Imthiyas; Thiyagarajan, Velumani; Arputharaj, Vijay J.; Thenmozhi, P.
International Journal of Engineering Trends and Technology;Volume;74;Issue;2;pp.223-234
https://doi.org/10.14445/22315381/IJETT-V74I2P116 https://www.scopus.com/pages/publications/105031864865?origin=resultslist
Banu I.I., Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, India; Thiyagarajan V., Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, India; Arputharaj V.J., Department of Computer Science, Christ University, Bengaluru, India; Thenmozhi P., Department Of Computer Science, Nandha Arts And Science College (Autonomous), Erode, India