Due to the massive rise of user-generated web content, particularly on social media networks, the amount of hate speech is steadily increasing. Hate speech online has been linked to a global increase in violence toward minorities, including mass shootings, lynchings, and ethnic cleansing.
This paper provides a comprehensive and structured overview of automatic hate speech detection, and compares few of its current approaches in a systematic manner, along presenting an insightful review of some published research on cyberbullying detection approaches.

Tweets Dataset for Detection of Cyber-Trolls obtained from DataTurks
Data Cleaning, Preprocessing (Word Tokenization, Stemming, TF-IDF), and Resampling was done before application of any of the Machine Learning algorithms used.

• Gaussian Naive Bayes
• Logistic Regression
• Decision Tree Classifier
• Adaboost Classifier
• Random Forest Classifier

The evaluation of the result shows that Ensemble supervised methods have the potential to perform better than traditional supervised methods. A number of directions forfuture work are also discussed.

Prakhar Bhasin

Kirtik Singh

Dev Kathuria

Ishank Nijhawan