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

Autores/as

  • 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

Palabras clave:

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

Resumen

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.

Descargas

Publicado

2022-05-04

Cómo citar

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

Número

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

Sección

Main Track Proceedings

Licencia

Derechos de autor 2022 Chih-yuan Li, Soon A. Chun, James Geller

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.