Detecting cyberbullying in social media using text analysis and ensemble techniques

Abstract

In the dynamic landscape of our hyper-connected digital world, social media platforms play a dual role as facilitators of global interaction and breeding grounds for harmful behaviors. Cyberbullying, an insidious online menace, inflicts emotional distress and psychological trauma on numerous individuals, underscoring the urgent need for advanced tools to detect and prevent such malevolent actions. This innovative project harnesses the power of artificial intelligence and text analysis to illuminate the dark corners of social media where cyberbullying thrives, offering hope to countless victims. At its core, this endeavor utilizes cutting-edge ensemble techniques, a fusion of diverse machine learning algorithms, to analyze textual content across social media platforms. This approach ensures unparalleled accuracy in identifying and flagging cyberbullying instances, enhancing the efficiency of the detection process while minimizing false positives. The project adopts a multifaceted approach to text analysis, examining explicit language, sentiments, context, and behavioral patterns in online interactions. By delving into the intricacies of human communication, the system distinguishes between genuine expressions and malicious intent, providing a nuanced and accurate assessment.