Detecting Cyber Bullies on Twitter using Machine Learning Techniques
Detecting Cyber Bullies on Twitter using Machine Learning Techniques

Summary/Abstract: The rising use of social networks leads to huge amount of user-generated data. Due to the popularity of social media cyber bullying has become a major problem. Cyber bullying may cause many serious impacts on a person’s life. In the existing system the set of unique features are derived from Twitter such as activity, user and tweet contents. By using these features the cyber bullying words which are presented in the tweets’ content are detected using machine learning algorithms such as Naïve Bayes and Random Forest classifiers. In the proposed work the detection of cyber bully words are integrated into a single unit. The name, gender and age of the cyber bullies will also be detected using feature extraction techniques. In this paper, the Naïve Bayes and Random Forest classifiers are used to detect cyber bullying content that is present in the tweets.

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