Leveraging Deep Neural Networks for Real-Time Detection of Cyber bullying and Offensive Memes in Social Networks

Cyberbullying and offensive content, particularly memes, are key challenges on social media that affect individuals and groups. Conventional moderation is ineffective because of the sheer and dynamic nature of online interactions. The current paper suggests a deep neural network (DNN)-powered system for real-time cyberbullying detection and offensive meme detection that combines natural language processing (NLP) and convolutional neural networks (CNNs). The system leverages a multi-modal DNN that handles both text and visual aspects. Pre-trained language models such as BERT and GPT process text, while CNNs detect toxic imagery. A fusion model combines these for accurate classification. Maintaining high accuracy with minimal false positives is a major challenge, overcome by a feedback loop that continually optimizes detection performance. Testing on several datasets demonstrates enhanced detection accuracy and efficiency compared to conventional methods. Transfer learning improves flexibility and decreases training time. The system classifies content in terms of severity so that extreme cases can be automatically removed while facilitating human inspection for borderline content. For fairness and transparency, explainable AI (XAI) methods offer transparent decision-making insights. This paper introduces a scalable and efficient content moderation solution, which saves significant harmful online interactions while upholding ethical AI practices.