Optimizing Deep Learning-Based Hate Speech Classification on Social Media Through Frame Semantic Theory and Hyperparameter Tuning

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DOI: https://doi.org/10.28924/2291-8639-24-2026-86
Published: Mar 19, 2026
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Korakoch Mounggam, Kittipol Wisaeng, Optimizing Deep Learning-Based Hate Speech Classification on Social Media Through Frame Semantic Theory and Hyperparameter Tuning, Int. J. Anal. Appl., 24 (2026), 86.

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Korakoch Mounggam, Kittipol Wisaeng

Abstract

This study uses deep learning, frame semantic theory, and precise hyperparameter tuning to classify social media hate speech. Three carefully selected hate speech datasets, arranged according to a frame semantic framework, are used to categorize opposing content, helping to better grasp context than simply looking for words. The Synthetic Minority Over-sampling Technique (SMOTE) enhanced minority-class detection to mitigate class imbalance. To find optimal configurations, deep learning architectures, activation functions, and data split algorithms were tested. The best approach used 70% of the data for training and 30% for testing, a model with hidden layers, a Rectifier activation function, and 10 epochs, achieving 96.30% accuracy, 96.95% recall, 99.15% precision, and 0.98 F1-score. These findings show that frame semantic theory, deep learning, and rigorous hyperparameter optimization can significantly improve social media hate speech detection systems. The method lays the foundation for context-aware and socially responsible content control.

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