Real-Time Browser-Based Early Warning System for Cyberbullying Detection in Online Platforms - Sonal Sharma; Manish Singh; Pratham Singh (International Journal of Computer Science Languages, 2026)
The rise in social networking through internet-based communication tools, Instagram, and YouTube, to name a few, significantly increases the risk of cyberbullying, thereby increasing psychological trauma on users, especially children, through adverse emotional states like anxiety, depression, etc. For a long time, researchers have been enhancing detection tools to counter cyberbullying, but their ability to detect only after the fact, along with limited support for English-based architecture, is a major concern that also compromises user privacy. This study seeks to develop a browser-based real-time warning system that can identify potentially harmful or offensive messages before they are transmitted. This study targets the development of a scheme that is multilingual, privacy-concerned, and enables users with warnings before posting messages that protect them from harm. The proposed methodology involves the usage of secondary data sets containing previously annotated data from social media sources like Instagram and YouTube, along with the survey method of questionnaire formation. Additionally, the proposed approach utilizes the lightweight multilingual transformer architecture for the detection of cyberbullying; the same architecture will be adapted for in-browser usage with the aid of TensorFlow.js so that the data never gets processed remotely from the client’s browser. The initial results also revealed the following insights regarding the competitive accuracy of the model, which could enable the reduction of inference latency: user responses also clearly pointed out the need for the model to cover the multilingual community, as well as the value of local data privacy. This study bridges the gap by using a combination of real-time detection, multi-lingual support, and a browser-based solution for privacy-safe deployment. The suggested algorithm aims at making the internet environment safer by offering a better guard against cyberbullying risks on various social media platforms.
Keywords: Privacy-preserving AI; browser-based system; multilingual analysis; natural language processing; real-time detection