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@article{171241,
        author = {S. Sivaraman and Nandhitha S and Dr. N. Mohanapriya},
        title = {Comprehensive Survey on Towards a Multi–Modal system for the detection of Cyberbullying and Fake Accounts in Social Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3462-3465},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171241},
        abstract = {As social networks increasingly dominate everyday life, cyberbullying has become a significant concern, disproportionately affecting younger individuals. Cyberbullying causes severe emotional harm, leading to distress, anxiety and long-term psychological trauma. Teens propose permanent bans and criminal charges to combat cyberbullying. The current cyberbullying detection system relies too heavily on text analysis, limiting its effectiveness. It struggles to incorporate non-textual data like images, videos and comments, hindering detection of complex cases. The existing cyberbullying detection system has major flaws, including limited scope and outdated technology. It struggles to detect complex cases, neglecting non-textual data and leaving vulnerable users exposed. A comprehensive, advanced system is urgently needed to address these shortcomings and effectively protect users. CyberGuard, an advanced system, detects cyberbullying through multi-modal analysis and fake account identification. It integrates text, image/video and behavioral analysis for accurate detection and real-time monitoring. CyberGuard enhances user safety, reduces false reports, and paves the way for future enhancements in social media security.},
        keywords = {Cyberbullying, Multi-modal analysis, Fake account identification, Behavioural analysis, Real-time monitoring, social media security},
        month = {December},
        }