Using Deep Learning Models to Detect Fake News about COVID-19

Mu Yen Chen, Yi Wei Lai, Jiunn Woei Lian

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)


The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of Coronavirus Disease (COVID-19), a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an "infodemic"by the World Health Organization (WHO), posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and compare them based on different text feature selections. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The long and short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the bidirectional long and short-term memory (BiLSTM) model were selected for fake news detection. BiLSTM produces the best detection result, with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82%.

期刊ACM Transactions on Internet Technology
出版狀態Published - 2023 5月 18

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信


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