DETECTION OF CYBERBULLYING ON SOCIAL MEDIA USING MACHINE LEARNING









Abstract

Increasing internet use and facilitating access to online communities such as social media have led to the emergence of cybercrime. Cyberbullying, a new form of bullying that emerged recently with the development of social networks, means sending messages that include slanderous statements, or verbally bullying other people in front of rest of the online community. The characteristics of online social networks enable cyberbullies to access places and countries that were previously unattainable for using SVM we are going to identify cyberbullying in twitter. Objectives of this implementation written in objective section. Image character with the help of OCR will be done by us to find image - based cyberbullying the impact on individual basis thus will be checked on dummy system. Machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. We are using natural language processing techniques and machine learning methods namely, Bayesian logistic regression, random forest algorithm, support vector machines have been used to determine cyberbullying.


Modules


Algorithms


Software And Hardware