Detection of Cyberbullying and Abusive Language on Social Media Using Supervised ML & NLP Techniques
International Journal of Mechanical Engineering · 2023
The introduction of the internet marked the beginning of the modern era of social networking.Nobody could have dreamed that the internet would eventually become home to such a large number of amazing services, including social networking, yet that is exactly what has happened.At this point in time, we are in a position to say that internet apps and social media platforms have become ingrained in people's everyday lives.Users of all ages spend significant amounts of time on these websites on a regular basis.Even if social media platforms make it possible for people to form emotional connections with one another, they also expose users to serious dangers, such as the threat of cyberattacks and the potential for online abuse.Cyberbullying is increasing in frequency alongside the proliferation of online social networking platforms.It is feasible to construct a machine learning (ML) model that can naturally recognise occurrences of social media bullying in order to find word associations in the tweets sent out by bullies.This can be accomplished by utilising machine learning.Nevertheless, there are many ways to identify abuse on social media, although most of them relied heavily on text.With this context and goal, developing appropriate methods to spot cyber abuse via social media can aid to prevent its occurrence.It is suggested that abuse on Twitter be identified and stopped using machine learning.The content of cyberbullying is trained and tested using naive Bayes.