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Real-Time Monitoring of Cyberbulling and Online Harassment

Gracy J, Kavya A, Shirisha V, Deepak N R, Rajesh Kumar Sahu

Abstract


Cyberbullying and Online Harassment the proliferation of Digital communication platforms and social media have facilitated substantial progress about unprecedented opportunities for global connectivity But has also led to the emergence of cyberbullying and online harassment. These forms of abuse can lead to severe emotional, psychological, and societal consequences, particularly among vulnerable populations. This paper presents a comprehensive review of the state-of-the-art techniques and methodologies for real-time monitoring and detection of cyberbullying and online harassment. The survey explores advancements in natural language processing (NLP), sentiment analysis, and machine learning models, such as deep learning and transformer-based architectures that has been employed to identify abusive behavior in textual, visual, and multimedia data.

Additionally, the paper evaluates the integration of real-time analytics, content moderation strategies, and ethical considerations in deploying such systems. Challenges such as dataset diversity, contextual understanding, and the balance between privacy and intervention are critically analyzed. This study offers an understanding of the current capabilities and limitations of existing solutions while highlighting a holistic appraisal of the effective, inclusive, and scalable systems to foster safer online environments.


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