A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection

Lida Ketsbaia, Biju Issac*, Xiaomin Chen, Seibu Mary Jacob

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Social media has revolutionized the way individuals connect and share information globally. However, the rise of these platforms has led to the proliferation of cyber-hate, which is a significant concern that has garnered attention from researchers. To combat this issue, various solutions have been proposed, utilizing Machine learning and Deep learning techniques such as Naive Bayes, Logistic Regression, Convolutional Neural Networks, and Recurrent Neural Networks. These methods rely on a mathematical approach to distinguish one class from another. However, when dealing with sentiment-oriented data, a more "critical thinking" perspective is needed for accurate classification, as it provides a more realistic representation of how people interpret online messages. Based on a literature review conducted to explore efficient classification techniques, this study applied two machine learning classifiers, Multinomial Naive Bayes and Logistic Regression, to four online hate datasets. The results of the classifiers were optimized using bio-inspired optimization techniques such as Particle Swarm Optimization and Genetic Algorithms, in conjunction with Fuzzy Logic, to gain a deeper understanding of the text in the datasets.
Original languageEnglish
Pages (from-to)56046-56065
Number of pages20
JournalIEEE Access
Early online date5 Jun 2023
Publication statusPublished - 2023

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