Identifying COVID-19 Fake News in Social Media
This work addresses the spread of misinformation during the COVID-19 pandemic, which can have harmful consequences, but it is incremental as it applies existing methods to a specific domain.
The authors tackled the problem of identifying COVID-19 fake news in social media by training models that classify health news as real or fake, achieving a high F1-score of 98.64% and securing second place on a leaderboard with a narrow margin of 0.05% points.
The evolution of social media platforms have empowered everyone to access information easily. Social media users can easily share information with the rest of the world. This may sometimes encourage spread of fake news, which can result in undesirable consequences. In this work, we train models which can identify health news related to COVID-19 pandemic as real or fake. Our models achieve a high F1-score of 98.64%. Our models achieve second place on the leaderboard, tailing the first position with a very narrow margin 0.05% points.