Tackling Cyberbullying: A Multilingual Approach to Cyberbullying Detection in India
Anam Bansal, Surinder Singh Khurana, Umar Farooq, Parvinder Pal Singh, Shahwar Nawshad · International Journal of Intelligent Systems Technologies and Applications · 2024
In India's evolving digital world, women are particularly vulnerable to cyberbullying due to differences in education, limited digital literacy, and pervasive cybersecurity risks. This research focuses on creating a system to detect cyberbullying targeting Indian women. Acknowledging the country's linguistic diversity, we adopt a multilingual approach, constructing a dataset incorporating English, Hindi-English swear words, common Indian slang, and offensive lexicons. We apply various machine learning algorithms to classify cyberbullying incidents. Upon evaluating the results, the relevance vector machine (RVM) algorithm emerged as the most effective, achieving 82.61% and 84.82% accuracy scores in detecting cyberbullying over English and Hinglish datasets, respectively. These findings provide crucial insights for crafting strategies to safeguard Indian women in the digital space. However, more sophisticated and hybrid models are planned for the future to address image, video, and audio-based cyberbullying against women.