COVID-19 fake news detection using attention-based transformer
Conference: ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application
12/17/2021 - 12/19/2021 at Shenyang, China
Proceedings: ICMLCA 2021
Pages: 7Language: englishTyp: PDF
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Authors:
Xi, Jiayang (School of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu, China)
Zhang, Chaokai (School of Engineering, University of Connecticut, Storrs, Connecticut, USA)
Abstract:
The worldwide epidemic, COVID-19, has resulted in the deaths of millions of people, and now coronavirus has gone globally. Meanwhile, misinformation about COVID-19 spread throughout the social networks like a virus, affecting social order during the pandemic. In this paper, three techniques, including attention-based transformer, CNN, and LSTM models, construct a COVID-19 fake news detector. The model performance parameters of accuracy, F1 score, AUC score, and training time involve evaluating the fake news detector. Furthermore, confusion matrix and ROC curve analysis have been employed to further analyze the attention-based transformer model's performance. Following the findings, the attention-based transformer model has shown its advantages through its high accuracy of 75.9% and AUC score of 0.774.