| Autor: |
Chong, Miyoung, Shah, Chirag, Shu, Kai, Jiangen, He, Hagen, Loni |
| Předmět: |
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| Zdroj: |
Proceedings of the Association for Information Science & Technology; Oct2022, Vol. 59 Issue 1, p555-558, 4p |
| Abstrakt: |
The circulation of myriad of information from diverse digital platforms during the COVID‐19 pandemic caused the unprecedented infodemic. Along with the increased case numbers, the shared information accelerated exponentially, especially via social media, and a large proportion of the daily distributed information was blended with myth, rumors, pseudoscience, or modified facts. Uncovering viral mis‐ and disinformation narratives and information voids is essential to a swift and effective response on delivering public health information and policy by the governments during a public health emergency. Although many studies have examined how information was circulated and shared during the COVID‐19 pandemic era, large gaps in literature exist as to how effectively to track, describe, and answer it. In this panel, the panelists propose and discuss data science methods to analyze the COVID‐19 infodemic. We hope our panel contribute to exploring more effective and applicable data science methods to investigate infodemic in crises. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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