Analysis of Cyber Bullying on Facebook Using Text Mining

Abstract

Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naïve Bayes algorithm. The score accuracy, classification report, and confusion matrix are also employed to assess the performance of the classifier. The accuracy of the classifier is 0.95(95%) which means the model can predict 95 of every 100 instances correctly. Also, the result of the experimental analysis shows that Naïve Bayes is effective in classifying a word into a bully or non-bully word and can identify the category of the bully word that is being sent online.