Authors: Amrutha Muralidhar
Affiliation: B. M. S College of Engineering, Bangalore


Keywords: Cyberbullying, Online Safety, Sentiment Analysis, Deep Learning, Text Classification
ABSTRACT. Social media has experienced exponential growth in recent years, becoming integral to daily communication and interaction. However, along with this growth, cyberbullying has emerged as a significant issue, causing harm and distress to individuals online. This paper investigates the effectiveness of utilizing BERT-based models for identifying cyberbullying behavior in online text. A BERT classifier was trained on a labeled dataset containing instances of cyberbullying and assessed for its performance in accurately detecting such behavior. Results indicate that the BERT classifier achieves a strong accuracy rate of 94% on the test dataset. These findings suggest the potential of BERT-based models in bolstering online safety efforts and combating cyberbullying. The aim of this study is to contribute to the advancement of tools aimed at fostering digital well-being and cultivating safer online communities


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