Alotaibi, M.; Alotaibi, B.; Razaque, A. A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics2021, 10, 2664.
Alotaibi, M.; Alotaibi, B.; Razaque, A. A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics 2021, 10, 2664.
Alotaibi, M.; Alotaibi, B.; Razaque, A. A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics2021, 10, 2664.
Alotaibi, M.; Alotaibi, B.; Razaque, A. A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics 2021, 10, 2664.
Abstract
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%.
Keywords
Online social networks (OSNs); Deep Learning; cyberbullying; Twitter
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
