Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

Muneer, A. and Alwadain, A. and Ragab, M.G. and Alqushaibi, A. (2023) Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT. Information (Switzerland), 14 (8). ISSN 20782489

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The prevalence of cyberbullying on Social Media (SM) platforms has become a significant concern for individuals, organizations, and society as a whole. The early detection and intervention of cyberbullying on social media are critical to mitigating its harmful effects. In recent years, ensemble learning has shown promising results for detecting cyberbullying on social media. This paper presents an ensemble stacking learning approach for detecting cyberbullying on Twitter using a combination of Deep Neural Network methods (DNNs). It also introduces BERT-M, a modified BERT model. The dataset used in this study was collected from Twitter and preprocessed to remove irrelevant information. The feature extraction process involved utilizing word2vec with Continuous Bag of Words (CBOW) to form the weights in the embedding layer. These features were then fed into a convolutional and pooling mechanism, effectively reducing their dimensionality, and capturing the position-invariant characteristics of the offensive words. The validation of the proposed stacked model and BERT-M was performed using well-known model evaluation measures. The stacked model achieved an F1-score of 0.964, precision of 0.950, recall of 0.92 and the detection time reported was 3 min, which surpasses the previously reported accuracy and speed scores for all known NLP detectors of cyberbullying, including standard BERT and BERT-M. The results of the experiment showed that the stacking ensemble learning approach achieved an accuracy of 97.4 in detecting cyberbullying on Twitter dataset and 90.97 on combined Twitter and Facebook dataset. The results demonstrate the effectiveness of the proposed stacking ensemble learning approach in detecting cyberbullying on SM and highlight the importance of combining multiple models for improved performance. © 2023 by the authors.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Computer crime; Deep neural networks; Learning algorithms; Natural language processing systems; Petroleum reservoir evaluation; Social networking (online), Bag of words; Continuous bag of word; Cyber bullying; Cyberbullying detection; Ensemble learning; Facebook; Language processing; Natural language processing; Natural languages; Social media; Stacked; Twitter; Word2vec; X platform, Learning systems
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 04 Oct 2023 12:44
Last Modified: 04 Oct 2023 12:44

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