Enhancing Hate Speech Detection in Low-Resource Code-Mixed Indonesian Tweets via GPT-Based Data Augmentation Article Swipe
Endang Wahyu Pamungkas
,
Dian Purworini
,
Widi Widayat
,
Divi Galih Prasetyo Putri
,
Ikhlasul Amal
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48084/etasr.14342
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48084/etasr.14342
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).
Related Topics
Concepts
Indonesian
Transformer
Computer science
Voice activity detection
Artificial intelligence
Scarcity
Social media
Speech recognition
Natural language processing
Encoder
Language model
Artificial neural network
Generative grammar
Emotion detection
Deep learning
Autoencoder
Machine learning
Feature extraction
Training set
Deep neural networks
Data modeling
Hidden Markov model
Recurrent neural network
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.48084/etasr.14342
- https://etasr.com/index.php/ETASR/article/download/14342/6045
- OA Status
- gold
- References
- 30
- OpenAlex ID
- https://openalex.org/W7111017995
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7111017995Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48084/etasr.14342Digital Object Identifier
- Title
-
Enhancing Hate Speech Detection in Low-Resource Code-Mixed Indonesian Tweets via GPT-Based Data AugmentationWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-08Full publication date if available
- Authors
-
Endang Wahyu Pamungkas, Dian Purworini, Widi Widayat, Divi Galih Prasetyo Putri, Ikhlasul AmalList of authors in order
- Landing page
-
https://doi.org/10.48084/etasr.14342Publisher landing page
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-
https://etasr.com/index.php/ETASR/article/download/14342/6045Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://etasr.com/index.php/ETASR/article/download/14342/6045Direct OA link when available
- Concepts
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Indonesian, Transformer, Computer science, Voice activity detection, Artificial intelligence, Scarcity, Social media, Speech recognition, Natural language processing, Encoder, Language model, Artificial neural network, Generative grammar, Emotion detection, Deep learning, Autoencoder, Machine learning, Feature extraction, Training set, Deep neural networks, Data modeling, Hidden Markov model, Recurrent neural networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| primary_location.source.host_organization_lineage_names | Engineering, Technology & Applied Science Research |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://etasr.com/index.php/ETASR/article/download/14342/6045 |
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| primary_location.raw_source_name | Engineering, Technology & Applied Science Research |
| primary_location.landing_page_url | https://doi.org/10.48084/etasr.14342 |
| publication_date | 2025-12-08 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3163314873, https://openalex.org/W3172559340, https://openalex.org/W3172414534, https://openalex.org/W3129361063, https://openalex.org/W3091315987, https://openalex.org/W4392728800, https://openalex.org/W4319988691, https://openalex.org/W4383426684, https://openalex.org/W4412921166, https://openalex.org/W3095259137, https://openalex.org/W2731703036, https://openalex.org/W4386236177, https://openalex.org/W2973089652, https://openalex.org/W2965466909, https://openalex.org/W3105668574, https://openalex.org/W2801887493, https://openalex.org/W4312428739, https://openalex.org/W4220755788, https://openalex.org/W4386690757, https://openalex.org/W4403311628, https://openalex.org/W4382201599, https://openalex.org/W4316021280, https://openalex.org/W2805807672, https://openalex.org/W2995252617, https://openalex.org/W4404783774, https://openalex.org/W2971296908, https://openalex.org/W3199849862, https://openalex.org/W4391558197, https://openalex.org/W4393405959, https://openalex.org/W4396768678 |
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