Detecting hate speech on Twitter

Sammanfattning: Hate speech and cyberbullying on social media platform Twitter is a grow-ing issue, and to combat this they turn to machine learning and computerscience. This study will investigate and compare different configurationsfor the naive Bayes classifier when classifying hate speech on Twitter. Wehave achieved a data set of nearly 13000 tweets, some containing hatespeech, and trained and tested our classifier with different configurations.The study shows that character level n-grams outperform word level n-grams, and the optimal size n-gram for character level is using combina-tions between 1-3.