Vol. 4 No. 1 (2025): January
RESEARCH ARTICLES

A Comprehensive Multi-Modal Framework For Cyberbullying Detection On Social Media

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  • M Suchithra,
  • M Sujan,
  • M Bramha Naidu,
  • K Asma,
  • M Lokesh,
  • C Venkata Subbaiah
M Suchithra
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
M Sujan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
M Bramha Naidu
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
K Asma
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
M Lokesh
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
C Venkata Subbaiah
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
DOI: https://doi.org/10.5281/zenodo.15123739

Published 2025-04-02

Keywords

  • Cyberbullying detection,
  • multi-modal framework,
  • social media,
  • natural language processing (NLP),
  • artificial intelligence (AI)

How to Cite

M Suchithra, M Sujan, M Bramha Naidu, K Asma, M Lokesh, & C Venkata Subbaiah. (2025). A Comprehensive Multi-Modal Framework For Cyberbullying Detection On Social Media. International Journal of Computational Learning & Intelligence, 4(1), 359–366. https://doi.org/10.5281/zenodo.15123739

Copyright (c) 2025 M Suchithra, M Sujan, M Bramha Naidu, K Asma, M Lokesh, C Venkata Subbaiah

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The pervasive use of social media has led to an alarming rise in cyberbullying, particularly among younger users, posing significant threats to mental and emotional well-being. Traditional approaches to cyberbullying detection have predominantly focused on textual analysis, which often fails to capture the multi-modal nature of bullying content, including images, videos, and contextual metadata. To address this limitation, we propose a novel multi-modal cyberbullying detection framework that integrates textual, visual, and contextual information to identify bullying behavior more effectively. Our approach leverages advanced deep learning techniques, including Hierarchical Attention Networks (HAN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, to model the complex interactions between different modalities. The framework processes user-generated posts, combining text and image data, along with metadata such as timestamps and user interactions, to predict whether a post constitutes cyberbullying. This research provides a robust, scalable solution for identifying and mitigating harmful content on social networks .
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