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Hate speech is a form of expression that assaults a person or a community based on race, origin, religion, sexual orientation, or other attributes. Although it can be expressed in multiple ways online and offline, the increasing popularity of social media has exponentially increased both its use and severity. The target of this research, therefore, is to locate and analyze the unstructured data of chosen social media posts that intend to spread hate in the comment sections. To do so, we propose a novel framework based on data science tools, with the objective of sensitizing all social media providers to the pervasiveness of hate on social media. We use sentiment and emotion algorithms to analyze recent posts and comments on those pages. Posts suspected of containing dehumanizing words are preprocessed before being fed to a K-means clustering algorithm, one of the simplest and most popular unsupervised machine learning methods. The proposed framework for automatic detection of hate speech (FADOHS) surpasses the most recent methods identified in A Survey on Automatic Detection of Hate Speech in Text in terms of precision, recall, and F1 scores by 10%.

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