Advanced Neural Network Approach for Detecting Cyberbullying and Hate Speech on Social Media: A Forensic Perspective

In recent years, social media platforms have become breeding grounds for harmful behaviours such as cyberbullying and hate speech. The increasing volume of user- generated content presents significant challenges for real-time detection and mitigation. This research proposes advanced neural network-based approaches for detecting cyberbullying and hate speech, with a specific focus on their application in social media forensics. We explore how deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can be adapted to analyse textual data from social media platforms, identifying harmful content while accounting for the complex, contextual nature of language. The study integrates uncertainty management into the detection process, enhancing the robustness of models by addressing ambiguities and nuances in online communication. By leveraging large-scale datasets of social media interactions, we develop a comprehensive framework capable of identifying a wide array of hate speech and cyberbullying scenarios, including those that evolve over time or are hidden within subtle linguistic cues. Keywords: Cyberbullying Detection, Hate Speech Detection, Natural Language Processing (NLP),DeepLearning,Ensemble