Cybershield Monitor Using Deep Learning and BERT Model

Cyberbullying has emerged as a significant issue in the digital age, impacting the mental health and well-being of individuals, particularly among youth. This paper presents a novel approach for detecting cyberbullying using deep learning techniques, specifically leveraging the BERT (Bidirectional Encoder Representations from Transformers) model. Our methodology involves collecting a comprehensive dataset of labeled text samples, encompassing instances of cyberbullying and non-cyberbullying comments. We preprocess the data by cleaning and tokenizing the text to prepare it for model training. Cyberbullying has become a pervasive issue on social media platforms, causing significant harm to individuals, particularly young people. This project proposes a novel approach to detect cyberbullying using a deep learning model based on the BERT (Bidirectional Encoder Representations from Transformers) algorithm. BERT is a state-of-the-art language model that has demonstrated exceptional performance in various natural language processing tasks. The proposed model leverages BERT’s ability to understand the context of text and capture semantic and syntactic information to effectively identify cyberbullying instances. The model is trained on a large dataset of social media posts, including both cyberbullying and non-cyberbullying content. During the training process, the model learns to identify patterns and features associated with cyberbullying, such as the use of abusive language, threats, and personal attacks. The experimental results demonstrate the effectiveness of the proposed model in accurately detecting cyberbullying with high precision and recall. The model outperforms existing approaches, particularly in handling complex and nuanced cases of cyberbullying. The proposed model has the potential to be a valuable tool for social media platforms and online communities to mitigate the negative impact of cyberbullying and promote a safer online environment.