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FAEO-ECNN: cyberbullying detection in social media platforms using topic modelling and deep learning

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Abstract

The widespread use of Social Media Platforms (SMP) such as Twitter, Instagram, Facebook, etc. by individuals has recently led to a remarkable increase in Cyberbullying (CB). It is a challenging task to prevent CB in such platforms since bullies use sarcasm or passive-aggressiveness strategies. This article proposes a new CB detection model named FAEO-ECNN for detecting and classifying cyberbullying on social media platforms. The proposed approach integrates Fuzzy Adaptive Equilibrium Optimization (FAEO) clustering-based topic modelling and Extended Convolutional Neural Network (ECNN) to enhance the accuracy of CB detection process. Initially, pre-processing is performed in order to cleanse the dataset. Next, the features are extracted using multiple models. The unsupervised Fuzzy Adaptive Equilibrium Optimization (FAEO) is utilized for discovering the latent topics from the pre-processed input data, which automatically examines the text data and creates clusters of words. Finally, the cyberbullying classification makes use of the ECNN and Rain Optimization (RO) algorithm to detect CB from posts/texts. We evaluated the proposed FAEO-ECNN thoroughly with two short text datasets: Real-world CB Twitter (RW-CB-Twitter) and Cyberbullying Menedely (CB-MNDLY) datasets in comparison to State of The Art (SoTA) models like Long Short Term Memory (LSTM), Bi-directional LSTM (BLSTM), RNN, and CNN-LSTM. The proposed FAEO-ECNN model outperformed the SoTA models in detecting Cyberbullying on SMP. It has obtained 92.91% of accuracy, 92.28% of recall, 92.53% of precision, and 92.40% of F-Measure over CB-MNDLY dataset. Moreover, it has achieved 91.89% of accuracy, 91.32% of recall, 91.81% of precision, and 91.56% of F-Measure on RW-CB-Twitter dataset.

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering and Information Technology, University of Amran, Amran, Yemen

    Belal Abdullah Hezam Murshed

  2. Department of Studies in Computer Science, University of Mysore, Mysore, Karnataka, 570006, India

    Belal Abdullah Hezam Murshed &  Suresha

  3. School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, 3220, Australia

    Jemal Abawajy

  4. Department of Computer Applications, Sri Jayachamarajendra College of Engineering, (Affiliated to VTU), Mysore, Karnataka, 570006, India

    Mufeed Ahmed Naji Saif

  5. Department of Computer Application, Ramaiah Institute of Technology, (Affiliated to VTU), Bangalore, 560054, India

    Hudhaifa Mohammed Abdulwahab

  6. Department of Computer Science and Engineering, PES College of Engineering, (Affiliated to Mysore University), Mandya, 571401, India

    Fahd A. Ghanem

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Contributions

Belal Abdullah Hezam Murshed: Methodology, Conceptualization, Validation, Data curation, Formal analysis, Investigation, Software, Visualization, Writing—Original Draft, Writing—Review & Editing. Suresha: Supervision, Methodology, Resources, Data curation. Jemal Abawajy: Methodology, Supervision, Formal analysis, Data curation, Writing—Review & Editing, Supervision. Mufeed Ahmed Naji: Investigation, Formal analysis, Data curation, Writing—Review & Editing. Hudhaifa Mohammed Abdulwahab: Resources, Writing—Review & Editing. Fahd A Ghanem: Resources, Writing—Review & Editing.

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Murshed, B.A.H., Suresha, Abawajy, J. et al. FAEO-ECNN: cyberbullying detection in social media platforms using topic modelling and deep learning. Multimed Tools Appl 82, 46611–46650 (2023). https://doi.org/10.1007/s11042-023-15372-3

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