Detection of Online Humiliation Through Social Media Platforms Using AI Inspired Algorithms

These days, it's impossible to imagine doing without the internet. Having access to a computer in real time is a clever, simple, and hilarious way to live. Alexa can quietly wake you up and initiate a variety of in-room conveniences to help you ease into your day. Another name for this is “real-time live computing,” and it facilitates a wide variety of communications and connections. Through online social networks, we may create a surrogate family that supports us even when we're thousands of miles apart. But there are always two sides to a story, and it was only a glimpse of the positive side. Because of this, young people are much more susceptible to online dangers. The primary objective of this dissertation was to review the literature on cybercrime, cyberbullying, and detection and prevention methods from the last several years. Second, we gathered data from over 35,000 tweets to feed to several intelligent machine learning algorithms, and then used five crucial ML algorithms to categorise and forecast whether tweets were offensive or not. Finally, a comparison of ML algorithms was made based on performance metrics.