A multichannel deep learning framework for cyberbullying detection on social media
Alotaibi M. Alotaibi B. Razaque A.
November-1 2021MDPI
Electronics (Switzerland)
2021#10Issue 21
Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.
Cyberbullying natural language processing (NLP) , Neural networks , Online social networks (OSNs) , Sentiment analysis , Twitter
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Department of Computer Science, Shaqra University, Shaqra, 11961, Saudi Arabia
Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia
Department of Computer Engineering & Cybersecurity, IITU, Almaty, 050000, Kazakhstan
Department of Computer Science
Sensor Networks and Cellular Systems Research Center
Department of Computer Engineering & Cybersecurity
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