Enhancing Hate Speech Detection in Low-Resource Code-Mixed Indonesian Tweets via GPT-Based Data Augmentation

Endang Wahyu Pamungkas; Dian Purworini; Widi Widayat; Divi Galih Prasetyo Putri; Ikhlasul Amal

doi:10.48084/etasr.14342

Authors

Endang Wahyu Pamungkas Department of Informatics Engineering, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia | Social Informatics Research Center, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia
Dian Purworini Department of Communication Science, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia | Social Informatics Research Center, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia
Widi Widayat Department of Informatics Engineering, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia | Social Informatics Research Center, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia
Divi Galih Prasetyo Putri Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia
Ikhlasul Amal Department of Artificial Intelligence, Universitas Gadjah Mada, Yogyakarta, Indonesia

Volume: 15 | Issue: 6 | Pages: 30649-30656 | December 2025 | https://doi.org/10.48084/etasr.14342

Received: 27 August 2025 | Revised: 29 September 2025 | Accepted: 6 October 2025 | Online: 21 November 2025

Corresponding author: Endang Wahyu Pamungkas

Abstract

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).

Keywords:

hate speech detection, low-resource language, code-mixed language, data augmentation, large language model

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