Cyberbullying Detection Modelling at Twitter Social Networking

Abstract

Cybercrimes often happened in social networking sites. Cyber-bullying is a form of cybercrime that recently trended in one of popular social networking sites, Twitter. The practice of cyber-bullying on teenager can cause depression, murderer or suicidal thoughts and it needs a preventing action so it will not harmful to the victim. To prevent cyber-bullying a text mining modelling can be done to classify tweets on Twitter into two classes, bullying class and not bullying class. On this research we use Naïve Bayes Classifier with five stages of pre-processing : replace tokens, transform case, tokenization, filter stopwords and n-grams. The validation process on this research used 10-Fold Cross Validation. To evaluate the performance of the model a Confusion Matrix table is used. The model on 10-Fold Cross Validation phase works well with 77,88% of precision , 94,75% of recall and 82,50% of accuracy with +/-5,12% of standard deviation.