Cyber Bullying Detection Using Deep Learning

Abstract

Abstract: Cyber bullying detection leveraging deep learning techniques. By harnessing the power of deep neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we aim to develop a robust and efficient model capable of accurately identifying instances of cyberbullying in textual and multimedia content. Through extensive experimentation on diverse datasets, we demonstrate the effectiveness of our proposed method in detecting subtle forms of online harassment with high precision and recall. This paper presents an approach for cyber bullying detection through keyword analysis. With the proliferation of online platforms, identifying instances of cyberbullying has become a pressing concern. Our method leverages a predefined set of keywords associated with bullying behavior to flag potentially harmful content. Through a combination of keyword matching and contextual analysis, we demonstrate the efficacy of our approachin accurately detecting cyberbullying instances across various digital communication channels. This keyword-based detection system offers a simple yet effective means of identifying and addressing cyberbullying.