Comparison of Machine Learning Algorithms for Hate and Offensive Speech Detection

Abstract

AbstractHate speech is not uncommon and is likely practiced almost on every networking platform. In recent times, due to exponential increase in Internet users and events such as the unprecedented pandemic and lockdown, it showed increased usage of social platforms for communicating thoughts, opinions, and ideas. Hate speech has a strong impact on people’s lives and is one of the reasons for suicidal events. There is certainly a strong need to make progress toward the mitigation of hate speech. Detection is the primary step to eradicate hate speech. In the following paper, the comparative analysis of different machine learning algorithms to detect hate speech was shown. Data from the Twitter social platform was considered. From the analysis, it was found that the long short-term memory method is a highly performant machine learning algorithm.KeywordsClassificationHate speechMachine learningNatural language processing