Emotion-Driven Adaptive Detection of Cyberbullying on Social Media Using Machine Learning

DOI

10.2991/978-94-6463-718-2_47How to use a DOI?

Keywords

Cyberbullying detection; emotion-driven learning; adaptive machine learning; social media analysis; multimodal data; real-time detection

Abstract

Social media cyberbullying has become a pressing social problem that brings about emotional and psychological damage to victims. Even though conventional machine learning approaches have proved their efficacy in detecting cyberbullying, they fail to adapt to dynamic user trends, subtle emotional contexts, and varying cultural ecosystem. To address these gaps, this research presents an emotion-driven adaptive detection framework for cyberbullying that uses the state-of-the-art machine learning techniques. With this technique, the presented model employs an innovation of a real-time emotional theoretical perspective, integrated multimodal data processing, and coherent adaptability methodologies to obtain overall high accuracy through numerous social media forums. Therefore, the identification of explicit and implicit cyberbullying patterns is ensured through emotion-based context recognition. Moreover, the model’s scalability and efficiency allow it to be deployed in real-world situations while minimizing and avoiding prejudices and cultural insensitivity. Its performance evaluation on several benchmark datasets shows substantial advancements in detection accuracy, adaptability, and real-time performance, positioning the suggested approach as a powerful answer to addressed cyberbullying in dynamic online surroundings.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Praveen Kumar
AU  - S. Srinivasan
AU  - S. Sucil Kumar
AU  - U. Kasthuri
AU  - E. Baby Anitha
AU  - M. Jayanthi
PY  - 2025
DA  - 2025/05/23
TI  - Emotion-Driven Adaptive Detection of Cyberbullying on Social Media Using Machine Learning
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 541
EP  - 552
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_47
DO  - 10.2991/978-94-6463-718-2_47
ID  - Kumar2025
ER  -