CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models
This addresses the problem of fake news spread on social media, particularly during the COVID-19 pandemic, but is incremental as it applies existing deep learning methods to a new dataset.
The research tackled COVID-19 misinformation detection on Twitter by creating the CovidMis20 dataset of 1,375,592 tweets and testing deep learning models, achieving up to 92.23% accuracy with an ensemble CNN+Bi-GRU model.
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.