Abstract
Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20.
This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model.
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Acknowledgments
This work was supported in part by Saginaw Valley State University and the National Science Foundation under Grant OAC-2017289, National Institute of Health under Grant 1R15GM120820-01A1, and WMU FRACAA 2012-22.
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Mulahuwaish, A., Osti, M., Gyorick, K., Maabreh, M., Gupta, A., Qolomany, B. (2023). CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets Using Deep Learning Models. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_47
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