Abstract
Cyberbullying is a growing concern in the digital age, affecting individuals of all ages and backgrounds. To compact this issue, various techniques have been developed for detecting and preventing cyberbullying. Cyberbullying detection involves the use of algorithm in machine learning and natural language processing (NLP) techniques to analyze online communication and identify instances of cyberbullying. These algorithms can be trained on datasets of labeled instances of cyberbullying, allowing them to recognize patterns and features in language that are indicative of bullying behavior. To address various concerns, it is important to balance the benefits of cyberbullying detection with the need to respect individual privacy and autonomy. This may involve develo** more nuanced and context-sensitive algorithms, as well as providing individuals with greater control over their online privacy and security.
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Lekshmi, M.S., Mariya Shaji, A., Amrita, S.K. (2024). Cyberbullying Detection Using BiLSTM Model. In: Gopi, E.S., Maheswaran, P. (eds) Proceedings of the International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. MDCWC 2023. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-47942-7_29
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