Transfer Learning of Pre-trained Transformers for Covid-19 Hoax Detection in Indonesian Language

Lya Hulliyyatus Suadaa(1*), Ibnu Santoso(2), Amanda Tabitha Bulan Panjaitan(3)

(1) Politeknik Statistika STIS
(2) Politeknik Statistika STIS
(3) Politeknik Statistika STIS
(*) Corresponding Author


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.


hoax detection, transfer learning, pre-trained transformer, Indonesian language text processing

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