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Detecting multimodal cyber-bullying behaviour in social-media using deep learning techniques

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Abstract

Cyberbullying detection refers to the process of classifying and identifying of cyberbullying behavior—which involves the use of technology to harass, or bullying individuals, typically through online platforms. A growing concern is the spread of bullying memes on social media, which can perpetuate harmful behavior. While much of the existing research focuses on detecting cyberbullying in text-based data, image-based cyberbullying has not received as much attention. This is a significant issue because many social media posts combine images with text, and the visual content can be a key component of cyberbullying. To address this, our research aims to develop a multimodal cyberbullying detection modal (MCB) that is capable of detecting bullying in both images and text. For this, we used VGG16 pretrained model to detect bullying in images and XLM-RoBERTa with BiGRU model to detect bullying in text. Together we integrated these models (VGG16 + XLM-RoBERTa and BiGRU) using attention mechanisms, CLIP, feedback mechanisms, CentralNet, etc., and proposed a model used for detecting cyberbullying in image-text-based memes. Our accomplished model produced a reasonable accuracy of 74%, pointing that the system is effective in recognizing most cyberbullying activity.

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Funding

The authors have not received any grants or funds during this work.

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Authors and Affiliations

  1. Department of CSE & IT, Narasaraopeta Engineering College (Autonomous), Narasaraopet, Palnadu, 522601, Andhra Pradesh, India

    Shaik MohammedJany & Shaik Rafi

  2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Green Fields, Vaddeswaram, Vijaywada, 522302, Andhra Pradesh, India

    Chandra Bhushana Rao Killi

  3. Department of Computer Science and Engineering, Narasaraopeta Engineering College (Autonomous), Narasaraopet, Palnadu, 522601, Andhra Pradesh, India

    Syed Rizwana

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  2. Chandra Bhushana Rao Killi
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  3. Shaik Rafi
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Contributions

Shaik rafi presented manuscript edited, methodology, and reviewed. KC Bhushanam reviewed. Mohammedjany performed manuscript edited and analysis. Syed Rizwana did reviewed and analysis

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Correspondence to Shaik Rafi.

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MohammedJany, S., Killi, C.B.R., Rafi, S. et al. Detecting multimodal cyber-bullying behaviour in social-media using deep learning techniques. J Supercomput 81, 284 (2025). https://doi.org/10.1007/s11227-024-06772-9

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