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Delving into Data Science Methods in Response to the COVID-19 Infodemic
Miyoung Chong1, Chirag Shah2, Kai Shu3
1University of Virginia USA.
Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology
|January 30, 2023
Summary
The COVID-19 pandemic created an infodemic, with social media amplifying myths and misinformation. Data science methods are proposed to effectively track and analyze this infodemic during public health crises.
Area of Science:
- Public Health
- Data Science
- Information Science
Background:
- The COVID-19 pandemic triggered a massive infodemic, characterized by the rapid spread of information, including misinformation and disinformation, across digital platforms.
- Social media platforms were primary channels for this information surge, often blending factual content with myths, rumors, and pseudoscience.
Purpose of the Study:
- To address the need for effective methods to track, describe, and analyze the COVID-19 infodemic.
- To propose and discuss data science approaches for understanding and combating health misinformation during public health emergencies.
Main Methods:
- Panel discussion focused on data science methodologies.
- Analysis of information circulation patterns during the COVID-19 pandemic.
Main Results:
- Identified significant gaps in the literature regarding effective infodemic tracking and analysis.
- Proposed data science methods as a crucial tool for investigating health crises.
Conclusions:
- Effective tracking and analysis of infodemics are essential for public health responses.
- Data science offers promising avenues for developing more applicable methods to investigate health-related information crises.
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