BULLYING AWARE :AN INTELLIGENT SYSTEM FOR EARLY DETECTION OF CYBERBULLYING USING MACHINE LEARNING

Abstract

Cyberbullying has become a pervasive issue in the digital era, particularly affecting adolescents and
young adults on social media platforms. Conventional detection methods, which rely on manual
reporting, are often slow, inconsistent, and ineffective. To the address this challenge, BullyingAware
introduces an intelligent machine learning-based system designed to automatically identify cyberbullying
content with high accuracy. Leveraging natural language processing (NLP) and supervised learning
algorithms, the system processes textual data through preprocessing techniques such as tokenization,
stemming, and stopword removal. Feature extraction methods, including TF-IDF and word embeddings,
enhance the model's ability to detect subtle linguistic cues, sarcasm, and contextual aggression.The
proposed system is trained on diverse datasets containing various forms of abusive language, ensuring
adaptability across different platforms.The Experimental evaluations to the demonstrate superior
performance in detecting cyberbullying, with high precision and recall rates compared to traditional
approaches.For the Future enhancements include real-time integration with social media platforms and
extending detection capabilities to the in the multimedia multimedia content such as images and videos.
By automating early detection, BullyingAware offers a scalable and efficient solution to combat
cyberbullying, promoting safer online interactions and mitigating its psychological impact on victims