Detecting COVID-19 Fake News on Social Media across Four Languages: Followers, Emotions, Relationships, and Uncertainty

  • CHIU, Ming Ming (PI)
  • KIM, Jeong-Nam (Collaborator)
  • EBERT, David (Collaborator)
  • OH, Yu Won (Collaborator)
  • PARK, Chong-Hyun (Collaborator)
  • LEE, Hyelim (Collaborator)
  • WANG, Zhan, Jan (Collaborator)
  • MORAKHOVSKI, Alex (Collaborator)

Project: Research project

Project Details


Fake news or misinformation related to the pandemic has misled people to eschew the vaccine, masks and social distancing, thus putting public health at risk. This research aims to create a first-generation COVID-19 information-assessment dashboard for publicly available, social media messages. The research will: 1. Develop information theory to identify COVID-19 tweets linked to fake news and its dissemination within and across networks; 2. Operationalise this theoretical model with artificial intelligence (AI) / machine learning (ML) and advanced statistics; 3. Determine how COVID-19 tweets spread within and across communities (scope, speed, and shape) and their antecedents across levels; and 4. Create an AI/ML-based dashboard to track the diffusion of COVID fake news tweets within and across online communities in real time.

Funding Source: RGC - Senior Research Fellow Scheme
Effective start/end date01/01/2331/12/27


  • Fake news, Social media, Machine learning, Statistical Discourse Analysis, Computational linguistics


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