A Review of Different Approaches to Detect Online Cyberbullying and Hate Speech
- DOI
- 10.2991/978-94-6463-716-8_76How to use a DOI?
- Keywords
- Cyberbullying; Inception V3; Support Vector Machine; Convolutional Neural Network
- Abstract
With the rapid rise in social media usage, platforms such as X(Twitter), Instagram and Facebook have become prevalent spaces for online interaction. These platforms allow users to share different forms of media such as text, image, video, etc. However, these platforms have also become a hotspot for harmful activities such as cyberbullying and hate speech. While these platforms employ solutions like machine learning models for text-based detection and deep learning, their ability to handle multi-modal content like images and videos remains limited. Our proposed solution introduces a web-based application that integrates multi-modal data analysis across the four major platforms: X(Twitter), Instagram and Facebook.
- 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.
Cite this article
TY - CONF AU - K. M. Chaman Kumar AU - Parth Mahajan AU - Tejas Naik AU - Navjyot Naik AU - Aryan Gaonkar PY - 2025 DA - 2025/05/26 TI - A Review of Different Approaches to Detect Online Cyberbullying and Hate Speech BT - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025) PB - Atlantis Press SP - 1017 EP - 1029 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-716-8_76 DO - 10.2991/978-94-6463-716-8_76 ID - Kumar2025 ER -