Evidence map›Paper›PMID 34866715›Full record

ArticleJournal of business research2022

Understanding patterns of COVID infodemic: A systematic and pragmatic approach to curb fake news.

Ashish Gupta, Han Li, Alireza Farnoush, Wenting Jiang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of business research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
27.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 91 citations in OpenAlex.

  1. Trial
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  9. Social Media, Anxiety and COVID-19 Lockdown Measurement Compliance.International journal of environmental research and public health · 2023
    Article
  10. Article
  11. Article
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  13. Review
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  17. Understanding Factors to COVID-19 Vaccine Adoption in Gujarat, India.International journal of environmental research and public health · 2022
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Ashish GuptaDepartment of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL 36849, USA.
Han LiDepartment of Marketing, Information Systems, Information Assurance, and Operations Management, Anderson School of Management, University of New Mexico, Albuquerque, NM 87106, USA.
Alireza FarnoushDepartment of Industrial Engineering, Samuel Ginn College of Engineering, Auburn University, USA.
Wenting JiangDepartment of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL 36849, USA.
Auburn University · USUniversity of New Mexico · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amid the flood of fake news on Coronavirus disease of 2019 (COVID-19), now referred to as COVID-19 infodemic, it is critical to understand the nature and characteristics of COVID-19 infodemic since it not only results in altered individual perception and behavior shift such as irrational preventative actions but also presents imminent threat to the public safety and health. In this study, we build on First Amendment theory, integrate text and network analytics and deploy a three-pronged approach to develop a deeper understanding of COVID-19 infodemic. The first prong uses Latent Direchlet Allocation (LDA) to identify topics and key themes that emerge in COVID-19 fake and real news. The second prong compares and contrasts different emotions in fake and real news. The third prong uses network analytics to understand various network-oriented characteristics embedded in the COVID-19 real and fake news such as page rank algorithms, betweenness centrality, eccentricity and closeness centrality. This study carries important implications for building next generation trustworthy technology by providing strong guidance for the design and development of fake news detection and recommendation systems for coping with COVID-19 infodemic. Additionally, based on our findings, we provide actionable system focused guidelines for dealing with immediate and long-term threats from COVID-19 infodemic.

Indexed as

COVID-19Fake NewsInfodemicNatural language processingNetwork analyticsText analytics

Identifiers

PMID34866715
PMCPMC8627595
OpenAlexW3213446454

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.