Published Paper Details:

Cyberbullying, the use of electronic devices to bully others, has become a prevalent issue globally. Identifying and preventing cyberbullying is crucial to protect individuals from its harmful effects. Cyberbullying is a pervasive and harmful phenomenon, causing significant emotional distress and psychological damage to victims. Cyberbullying has become a major issue in today's society, especially among young individuals who are constantly using social media platforms. Machine learning (ML) algorithms offer a powerful tool for automating cyberbullying detection to mitigate this issue effectively. This paper explores the potential of machine learning algorithms to automatically detect cyberbullying in online platforms. In order to address this problem, this paper proposes a machine learning-based approach for detecting instances of cyberbullying in online platforms. By leveraging the power of machine learning algorithms, we aim to accurately identify and classify cyberbullying behaviour, leading to more effective mitigation strategies and interventions. We discuss various algorithms and their applications in text analysis, focusing on their strengths and weaknesses in identifying cyberbullying content. We also delve into the challenges and ethical considerations associated with employing machine learning for this purpose. We propose a method for detecting cyberbullying using machine learning algorithms. We discuss the challenges associated with accurately identifying cyberbullying behaviour and how machine learning can be leveraged to effectively detect and prevent such behaviour. We present an experimental evaluation of our proposed method and demonstrate its effectiveness in detecting cyberbullying.