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Article

A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

1
Department of Computer Science, Shaqra University, Shaqra 11961, Saudi Arabia
2
Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
3
Department of Computer Engineering & Cybersecurity, IITU, Almaty 050000, Kazakhstan
*
Authors to whom correspondence should be addressed.
Academic Editor: Martin Reisslein
Electronics 2021, 10(21), 2664; https://doi.org/10.3390/electronics10212664
Received: 3 October 2021 / Revised: 25 October 2021 / Accepted: 26 October 2021 / Published: 31 October 2021
(This article belongs to the Special Issue Big Data Privacy-Preservation)
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%. View Full-Text
Keywords: Online social networks (OSNs); sentiment analysis; cyberbullying natural language processing (NLP); neural networks; Twitter Online social networks (OSNs); sentiment analysis; cyberbullying natural language processing (NLP); neural networks; Twitter
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MDPI and ACS Style

Alotaibi, M.; Alotaibi, B.; Razaque, A. A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics 2021, 10, 2664. https://doi.org/10.3390/electronics10212664

AMA Style

Alotaibi M, Alotaibi B, Razaque A. A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics. 2021; 10(21):2664. https://doi.org/10.3390/electronics10212664

Chicago/Turabian Style

Alotaibi, Munif, Bandar Alotaibi, and Abdul Razaque. 2021. "A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media" Electronics 10, no. 21: 2664. https://doi.org/10.3390/electronics10212664

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