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Combating the Infodemic: A Chinese Infodemic Dataset for Misinformation Identification.

Jia Luo1,2, Rui Xue1, Jinglu Hu2

  • 1College of Economics and Management, Beijing University of Technology, Beijing 100124, China.

Healthcare (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study constructs "infodemic 2019", a balanced Chinese dataset of COVID-19 misinformation from social media. It provides labeled data for infodemic detection research, crucial for understanding public health crises.

Keywords:
COVID-19deep learninginfodemic datamisinformation identification

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Area of Science:

  • Public Health
  • Data Science
  • Computational Social Science

Background:

  • The COVID-19 pandemic saw a surge in online misinformation, termed an infodemic.
  • Publicly available datasets for infodemic detection, especially from China, are scarce.
  • Manual construction of such datasets is time-consuming and challenging.

Purpose of the Study:

  • To construct a comprehensive Chinese infodemic dataset during the COVID-19 outbreak.
  • To label collected social media data as true, false, or questionable.
  • To create a balanced dataset for robust infodemic detection model training.

Main Methods:

  • Collected widely spread Chinese infodemic data from social media platforms.
  • Labeled each record with expert annotations, achieving high intercoder reliability.
  • Adjusted the dataset to achieve balance and analyzed high-frequency words for relevance.

Main Results:

  • Developed the "infodemic 2019" dataset, a balanced collection of Chinese COVID-19 misinformation.
  • High intercoder reliability was achieved with healthcare worker annotations.
  • High-frequency words in the dataset strongly correlate with pandemic diseases.

Conclusions:

  • The "infodemic 2019" dataset is a valuable resource for infodemic detection research.
  • The dataset provides a baseline for evaluating machine learning models like RNN, CNN, and fastText.
  • This work addresses the critical need for accessible infodemic data in public health crises.