Combating the infodemic: A chinese infodemic dataset for misinformation identification

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

*この研究の対応する著者

研究成果: Article査読

抄録

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.

本文言語English
論文番号1094
ジャーナルHealthcare (Switzerland)
9
9
DOI
出版ステータスPublished - 2021 8

ASJC Scopus subject areas

  • 健康情報学
  • 健康政策
  • 健康情報管理
  • リーダーシップと管理

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