https://www.irjmets.com/uploadedfiles/paper//issue_3_march_2024/51299/final/fin_irjmets1711566124.pdf
International Research Journal of Modernization in Engineering Technology and Science · 2024
This paper presents a novel cyberbullying detection and prevention tool leveraging Long Short-Term Memory (LSTM) networks.Cyberbullying has become a pervasive issue in online platforms, necessitating advanced techniques for timely detection and intervention.Our tool employs LSTM models, renowned for their capability to capture sequential patterns in text data, to analyze and classify potentially harmful content in Social media platforms accurately.By training on large datasets of annotated cyberbullying instances, the LSTM-based system learns to discern nuanced linguistic cues indicative of cyberbullying behavior.Furthermore, the tool incorporates a real-time monitoring mechanism that continuously scans online content, promptly flagging instances of cyberbullying for immediate intervention.Through a combination of natural language processing techniques and deep learning methodologies, our system offers an effective means of combating cyberbullying, fostering safer online environments for users of all ages.