A Survey on Monitoring and Detecting Cyber Bullying Activities using Machine Learning Algorithms

Abstract

The shrinking of the planet by technology is causing new age difficulties in youth culture. Technology surely has a lot of benefits, but it also has risks. It is where cyberbullying first started. Thus, there are many different types of cyberbullying. It might not necessarily involve pretending to be someone else or breaking into their online accounts. It also includes criticizing someone or spreading lies about them in an effort to cast doubt on them. Social media is widely used, making it incredibly easy for anyone to misuse this access. Cyberbullying is a serious issue today. It includes actions that harass, mislead, or defame someone. These violent behaviors are incredibly hazardous and can harm anyone quickly and severely. They appear on open discussion forums, social media sites, and other internet chat boards. A cyberbully is not always an anonymous person; they could be someone you know. The detection of online cyberbullying has grown in societal significance, research interest, and accessibility of open data. Even so, despite the continued rise in processing power and resource affordability, access limitations to high quality data constrain the use of cutting-edge methodologies. As a result, many recent studies use limited, heterogeneous datasets without fully assessing their usefulness. This study discusses effective techniques used to detect online abusive and bullying messages by merging natural language processing and machine learning algorithms with distinct features to analyze the accuracy levels of the algorithms.