Combating the infodemic: A chinese infodemic dataset for misinformation identification

Jia Luo, Rui Xue*, Jinglu Hu, Didier El Baz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains rare and is not easy to construct manually. Therefore, instead of developing techniques for infodemic detection, this paper aims at constructing a Chinese infodemic dataset, “infodemic 2019”, by collecting widely spread Chinese infodemic during the COVID-19 outbreak. Each record is labeled as true, false or questionable. After a four-time adjustment, the original imbalanced dataset is converted into a balanced dataset by exploring the properties of the collected records. The final labels achieve high intercoder reliability with healthcare workers’ annotations and the high-frequency words show a strong relationship between the proposed dataset and pandemic diseases. Finally, numerical experiments are carried out with RNN, CNN and fastText. All of them achieve reasonable performance and present baselines for future works.

Original languageEnglish
Article number1094
JournalHealthcare (Switzerland)
Volume9
Issue number9
DOIs
Publication statusPublished - 2021 Aug

Keywords

  • COVID-19
  • Deep learning
  • Infodemic data
  • Misinformation identification

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy
  • Health Information Management
  • Leadership and Management

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