Citation :

I. Imthiyas Banu, Velumani Thiyagarajan, Vijay Arputharaj J, P. Thenmozhi, "Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier For Hate Speech Detection in Online Social Network," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 2, pp. 223-234, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I2P116

Abstract

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.

Keywords

Hate Speech Detection, Deep Belief Networks and Convolutional Neural Networks, Canonical Correlation, Krill Herd Algorithm.

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