Quasi-Reflection Learning Arithmetic Firefly Search Optimization with Deep Learning-based Cyberbullying Detection on Social Networking

Authors

  • Ahmad Taher Azar College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia | Faculty of Computers and Artificial Intelligence, Benha University, Egypt
  • Harith Muthanna Noori Department of Cybersecurity and Cloud Computing Technical Engineering, Uruk University, Baghdad, Iraq
  • Ahmed Redha Mahlous College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
  • Ahmed Al-Khayyat College of Technical Engineering, The Islamic University, Najaf, Iraq | College of Technical Engineering, The Islamic University of Al Diwaniyah, Iraq | College of Technical Engineering, The Islamic University of Babylon, Iraq
  • Ibraheem Kasim Ibraheem Department of Electrical Engineering, College of Engineering, University of Baghdad, Iraq
Volume: 14 | Issue: 5 | Pages: 17162-17169 | October 2024 | https://doi.org/10.48084/etasr.8314

Abstract

Social networks are a major medium for communicating, collaborating, and sharing knowledge, data, and ideas. However, due to anonymity preservation, incidents of cyberbullying and hate speech emerge. Cyberbullying is very common on social media, and people end up with depression and do not take action against it. Automatic identification of these situations on many social networking sites requires intelligent systems. Deep learning (DL) methods are preferred for their potential in text classification, with accurate results on various academic benchmark issues. This study develops a Quasi-reflection Learning Arithmetic Firefly Search Optimization with Deep Learning Cyberbullying Detection (QLAFSO-DLCBD) technique to detect accurately cyberbullying on social media. The proposed QLAFSO-DLCBD method undergoes an initial preprocessing stage to convert the raw data into a meaningful format. The Keras embedding layer is used for word embedding purposes. The QLAFSO-DLCBD technique applies the Attention-based Bidirectional Long Short-Term Memory (ABiLSTM) method to detect cyberbullying. The QLAFSO algorithm was employed to select optimal hyperparameters for the ABiLSTM method, enhancing detection performance. Extensive experimental and comparative results suggest a higher efficacy of the proposed QLAFSO-DLCBD method compared to other recent methods.

Keywords:

metaheuristics, cyberbullying detection, natural language processing, social media, deep learning

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How to Cite

[1]
Azar, A.T., Noori, H.M., Mahlous, A.R., Al-Khayyat, A. and Ibraheem, I.K. 2024. Quasi-Reflection Learning Arithmetic Firefly Search Optimization with Deep Learning-based Cyberbullying Detection on Social Networking. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 17162–17169. DOI:https://doi.org/10.48084/etasr.8314.

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