Abusive Content Detection using Deep Learning: A BERT-Based Approach

The rise of social media platforms on the internet has made combating hate speech and other abusive content more challenging. In this work, we investigate the effectiveness of deep learning techniques for abusive content detection. In particular, we applied the modern learning model BERT. In order to accurately identify abusive language in a variety of textual data types, such as social media posts and online comments, our research focuses on optimizing pre-trained BERT models. We demonstrate, via rigorous testing, that the BERT-based strategy could substantially improve the efficiency of classification with respect to conventional machine learning methods. In conclusion, this research contributes to ongoing efforts to prevent cyberbullying and foster positive interactions with others online. By leveraging cutting edge methods like BERT, we aim to equip online platforms with powerful tools for abusive behavior detection and remediation. The study's findings provide important novel data into the practical applications of deep learning for abuse content detection, which in turn opens up new avenues for future improvements in online safety measures.