Fuzzy and Machine learning Classifiers for Hate Content Detection: A Comparative Analysis

Hate content on social media is currently one of the most significant risks, where the victim is either a single individual or a group of people. In the current scenario, online web platforms are one of the most prominent ways to contribute to an individual's opinions and thoughts. Free sharing of ideas on an event or situation also bulks on the web. Information sharing is sometimes a bane for society if primarily used platforms are utilized with some lousy intention to spread hatred for intentionally creating chaos/ confusion among the public. Users take this as an opportunity to spread hate to get some monetary benefits, the detection of which is of paramount importance. This article includes various fuzzy pattern classifiers, including both the top-down and bottom-up algorithms for identifying the hate contents on multiple datasets, compared to the baseline results obtained from diverse machine learning or deep learning classifiers. Moreover, the result shows that fuzzy logic classifiers give decent results when classification is done on hate speech datasets.