Stemming the Tide of Fake News about the COVID-19 Pandemic

Auteurs-es

  • Chih-yuan Li New Jersey Institute of Technology
  • Soon Chun College of Staten Island
  • James Geller NJIT

DOI :

https://doi.org/10.32473/flairs.v35i.130716

Mots-clés :

Fake News Identification, COVID-19, Machine Learning, Sentiment Analysis, Infodemic, Metadata Tags

Résumé

While the world has been combating COVID, there has also been an ongoing “Infodemic,” caused by the spread of fake news about the pandemic. Due to the rapid data sharing on social media, the impact of fake news can be quite damaging. Citizens might mistake fakes news for real news. Human lives have been lost due to fake information about COVID. Our goal is to identify fake news on social media and help stem the spread by deep learning approaches. To understand the different characteristics in fake and real news, we conducted behavioral and sentiment analyses between fake and real news regarding the COVID pandemic. We then further built detection models based on feature elimination, and we identified differences of model robustness based on selected features.

Téléchargements

Publié-e

2022-05-04

Comment citer

Li, C.- yuan, Chun, S., & Geller, J. (2022). Stemming the Tide of Fake News about the COVID-19 Pandemic. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130716

Numéro

Vol. 35 (2022): Proceedings of FLAIRS-35

Rubrique

Main Track Proceedings

Licence

© Chih-yuan Li, Soon A. Chun, James Geller 2022

Licence Creative Commons

Cette œuvre est sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale 4.0 International.