Transfer Learning of Pre-trained Transformers for Covid-19 Hoax Detection in Indonesian Language
Nowadays, internet has become the most popular source of news. However, the validity of the online news articles is difficult to assess, whether it is a fact or a hoax. Hoaxes related to Covid-19 brought a problematic effect to human life. An accurate hoax detection system is important to filter abu...
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| Main Authors: | , , |
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| Format: | EJournal Article |
| Published: |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2021-07-31.
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| Subjects: | |
| Online Access: | Get Fulltext Get Fulltext |
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| Summary: | Nowadays, internet has become the most popular source of news. However, the validity of the online news articles is difficult to assess, whether it is a fact or a hoax. Hoaxes related to Covid-19 brought a problematic effect to human life. An accurate hoax detection system is important to filter abundant information on the internet. In this research, a Covid-19 hoax detection system was proposed by transfer learning of pre-trained transformer models. Fine-tuned original pre-trained BERT, multilingual pre-trained mBERT, and monolingual pre-trained IndoBERT were used to solve the classification task in the hoax detection system. Based on the experimental results, fine-tuned IndoBERT models trained on monolingual Indonesian corpus outperform fine-tuned original and multilingual BERT with uncased versions. However, the fine-tuned mBERT cased model trained on a larger corpus achieved the best performance. |
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| Item Description: | https://jurnal.ugm.ac.id/ijccs/article/view/66205 |