Deep Learning Approaches for Detecting Cyberbullying on Social Media

The widespread use of social media has brought many challenges, mainly due to a misconstrued interpretation of the right to freedom of expression. Cyberbullying is a particularly noteworthy issue with far-reaching global implications for both its victims and the wider community. It takes the form of bullying that happens on several social media websites. This paper's goal is to develop a deep learning model capable of recognizing cases of cyberbullying on social media. Four models, such as bidirectional long short-term memory (BiLSTM), convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM), bidirectional long short-term memory with gated recurrent unit (BiLSTM-GRU), and artificial neural network (ANN), will be evaluated in a multiclass classification difficulty context. The results showed that the BiLSTM model outperformed the other models by achieving the highest accuracy in 91% of cases, while the CNN-BiLSTM and ANN models demonstrated relatively lower performance. In addition to determining the efficacy of the deep learning techniques, the work highlights the urgent requirement for strong systems to resist cyberbullying. By enhancing detection accuracy, the proposed model can contribute significantly to providing a safer digital environment for further studies in this field. Received: 24 August 2024 | Revised: 16 December 2024 | Accepted: 25 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ghaith Jaradat: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Dyala Ibrahim: Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration. Saif Najdawi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Rami Sihwail: Software, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.

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Publication Details

Journal
Journal of Computational and Cognitive Engineering
Published
2025-03-31
DOI
https://doi.org/10.47852/bonviewjcce52024162
Citations
5
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
12.40
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article

Deep Learning Approaches for Detecting Cyberbullying on Social Media

Dyala Ibrahim, Mohammad Shehab, Ghaith M. Jaradat, Rami Sihwail et al.
5 citations
Journal of Computational and Cognitive Engineering
Hate Speech and Cyberbullying Detection
12.40
article

Deep Learning Approaches for Detecting Cyberbullying on Social Media

Dyala Ibrahim, Mohammad Shehab, Ghaith M. Jaradat, Rami Sihwail, Saif Najdawi
article en
5 citations

Abstract

The widespread use of social media has brought many challenges, mainly due to a misconstrued interpretation of the right to freedom of expression. Cyberbullying is a particularly noteworthy issue with far-reaching global implications for both its victims and the wider community. It takes the form of bullying that happens on several social media websites. This paper's goal is to develop a deep learning model capable of recognizing cases of cyberbullying on social media. Four models, such as bidirectional long short-term memory (BiLSTM), convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM), bidirectional long short-term memory with gated recurrent unit (BiLSTM-GRU), and artificial neural network (ANN), will be evaluated in a multiclass classification difficulty context. The results showed that the BiLSTM model outperformed the other models by achieving the highest accuracy in 91% of cases, while the CNN-BiLSTM and ANN models demonstrated relatively lower performance. In addition to determining the efficacy of the deep learning techniques, the work highlights the urgent requirement for strong systems to resist cyberbullying. By enhancing detection accuracy, the proposed model can contribute significantly to providing a safer digital environment for further studies in this field. Received: 24 August 2024 | Revised: 16 December 2024 | Accepted: 25 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ghaith Jaradat: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Dyala Ibrahim: Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration. Saif Najdawi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Rami Sihwail: Software, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.

Journal of Computational and Cognitive EngineeringVol. 4(4)
Amman Arab University (JO)
Openalex Percentile: Top 2%
Hate Speech and Cyberbullying Detection
12.40
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