What question did this study set out to answer?

This research aims to evaluate the effectiveness of various machine learning models in classifying hate speech online.

April 1, 2026JOURNAL OF ADVANCE AND FUTURE RESEARCHOpen Access

Comparative Evaluation of Machine Learning Models for Multi-Modal Classification of Hate Speech

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Authors

MMM D MaheshIndian Institute of Technology Tirupati YKYanala keerthanaIndian Institute of Technology Tirupati

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Implication

Comparative evaluation demonstrates improved hate speech detection accuracy using SVM in online environments, suggesting better moderation strategies.

Key Points

This research aims to evaluate the effectiveness of various machine learning models in classifying hate speech online.
Developed a multi-modal hate speech detection system using supervised machine learning approaches.
Utilized Naïve Bayes and Support Vector Machine (SVM) for classification of textual input.
Employed feature extraction techniques on a labeled dataset consisting of hateful and non-hateful content.
Conducted performance comparison using confusion matrix analysis.
SVM model exhibited significantly higher accuracy compared to Naïve Bayes classifier.
Reduced misclassification rates were observed with the SVM model.
The framework successfully decreased false positives while improving identification of hateful content.

Cite This Study

Mahesh et al. (2026) studied this question.

synapsesocial.com/papers/69ccb6e416edfba7beb88b71 https://doi.org/10.56975/jaafr.v4i3.505419

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Implication

Comparative evaluation demonstrates improved hate speech detection accuracy using SVM in online environments, suggesting better moderation strategies.

Comparative Evaluation of Machine Learning Models for Multi-Modal Classification of Hate Speech

Discussion

Member takes

Implication

Key Points

Cite This Study

Comparative Evaluation of Machine Learning Models for Multi-Modal Classification of Hate Speech

Discussion

Member takes

Implication

Key Points

Cite This Study

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