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Enhancing Hate Speech Detection in LowResource Code-Mixed Indonesian Tweets via GPT-Based Data Augmentation.

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Název: Enhancing Hate Speech Detection in LowResource Code-Mixed Indonesian Tweets via GPT-Based Data Augmentation.
Autoři: Pamungkas, Endang Wahyu, Purworini, Dian, Widayat, Widi, Putri, Divi Galih Prasetyo, Amal, Ikhlasul
Zdroj: Engineering, Technology & Applied Science Research; Dec2025, Vol. 15 Issue 6, p30649-30656, 8p
Témata: DATA augmentation, TRANSFORMER models, MACHINE learning, INDONESIAN language, PARAPHRASE, LOW-resource languages, SOCIAL media, INVECTIVE
Abstrakt: Automatic hate speech detection in low-resource, code-mixed languages, such as Indonesian social media environments, presents significant challenges due to the scarcity of annotated data and the linguistic variability introduced by code-mixing. However, due to the growing prevalence of hate speech on social media, there is a need for robust hate speech detection systems. This study investigates the effectiveness of data augmentation strategies, specifically Generative Pretrained Transformer (GPT)-based paraphrasing and aggressive text transformation, in enhancing the performance of hate speech detection models for Indonesian code-mixed tweets. To achieve that, we employed traditional machine learning models, Recurrent Neural Network (RNN)-based models, and transformer-based models to assess the impact of these augmentation strategies. Our findings reveal that GPT-generated data improve model performance, with transformer-based models, including Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) and the Cross-lingual Language Model Robustly Optimized BERT Pretraining approach (XLM-RoBERTa). [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Automatic hate speech detection in low-resource, code-mixed languages, such as Indonesian social media environments, presents significant challenges due to the scarcity of annotated data and the linguistic variability introduced by code-mixing. However, due to the growing prevalence of hate speech on social media, there is a need for robust hate speech detection systems. This study investigates the effectiveness of data augmentation strategies, specifically Generative Pretrained Transformer (GPT)-based paraphrasing and aggressive text transformation, in enhancing the performance of hate speech detection models for Indonesian code-mixed tweets. To achieve that, we employed traditional machine learning models, Recurrent Neural Network (RNN)-based models, and transformer-based models to assess the impact of these augmentation strategies. Our findings reveal that GPT-generated data improve model performance, with transformer-based models, including Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) and the Cross-lingual Language Model Robustly Optimized BERT Pretraining approach (XLM-RoBERTa). [ABSTRACT FROM AUTHOR]
ISSN:22414487
DOI:10.48084/etasr.14342