ArticleProceedings of the Association for Information Science and Technology. Association for Information Science and Technology2022
Delving into Data Science Methods in Response to the COVID-19 Infodemic.
Article in Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.