Hate Speech Detection Using Text Mining and Machine Learning

Safae Alaoui, Yousef Farhaoui, Brahim Aksasse

OAI: oai:igi-global.com:286680 • DOI: 10.4018/IJDSST.286680

Automatic hate speech detection on social media is becoming an outstanding concern in modern countries. Indeed, hate speech towards people brings about violent acts and social chaos; hence, law prohibits it, and it engenders moral and legal implications. It is crucial that we can precisely categorize hate speech and not hate speech automatically. This allows us to identify easily real people who represent a threat for our society. In this paper, the authors applied a complete text mining process and naïve bayes machine learning classification algorithm to two different data sets (tweets_Num1 and tweets_Num2) taken from Twitter to better classify tweets. The results obtained demonstrate that the model performed well regarding different metrics based on the confusion matrix including the accuracy metric, which achieved 87. 23% on the first dataset and 93. 06% on the second.

Decision Support Systems Computer Science and Information Technology Decision Support Systems