Cited by 95Misinformation and Its ImpactsCommunication and COVID-19 ImpactPublic Relations and Crisis Communication

Understanding misinformation and rumors that generated panic buying as a social practice during COVID-19 pandemic: evidence from Twitter, YouTube and focus group interviews

Naeem, Muhammad, Ozuem, Wilson·2022·Information Technology & People

Abstract

Purpose: The purpose of the study is to understand how socially shared misinformation and rumors can enhance the motivation to protect personal interests and enhance social practices of panic buying. Design/methodology/approach: The study employed a number of qualitative data collection methods for the purpose of triangulation, as it can offer thick interpretation and can help to develop a context specific research framework. Findings: The shared misinformation and rumors on social media developed into psychological, physical and social threats; therefore, people started panic buying to avoid these negative consequences. People believed that there were differences between the information shared by politicians and government officials and reality, such as “everything is under control,” whereas social media showed people standing in long queues and struggling to buy the necessities of life. The shared misinformation and rumors on social media became viral and received social validation, which created panic buying in many countries. Research limitations/implications: It is the responsibility of government, politicians, leaders, media and the public to control misinformation and rumors, as many people were unable to buy groceries due either to socio-economic status or their decisions of late buying, which increased depression among people. Originality/value: The study merged the theory of rumor (TORT) transmission and protection motivation theory (PMT) to understand how misinformation and rumors shared through social media increased global uncertainty and the desire to panic buy across the world.

Integrity signals

Registry

Crossref · 10.1108/itp-01-2021-0061

Cites

92 of 92 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The resulting themes provide understanding for constructing a coherent narrative that connects the quotations, keywords and themes together; this enables a thorough understanding of the data and helps to answer the research question.

    “The resulting themes provide understanding for constructing a coherent narrative that connects the quotations, keywords and themes together; this enables a thorough understanding of the data and helps to answer the research question (Naeem, 2021).”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Naeem, M., & Ozuem, W.
Affiliations
  • University of Gloucestershire (GB)
  • University of Cumbria (GB)
Venue
Information Technology & People
Publisher
Emerald
Published
2021-10-27
Volume
35
Issue
7
Pages
2140-2166
ISSN
0959-3845 (ISSN-L)
OpenAlex
W3210966448
Cited by
94
References
92
Citations by year
2026: 14 · 2025: 22 · 2024: 30 · 2023: 13 · 2022: 15
FWCI
20.10
Type
journal article
Language
en
Open access
closed
Locations
  • Information Technology and People (publishedVersion)
  • Insight (University of Cumbria) (acceptedVersion)
  • Research Repository (University of Gloucestershire) (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Communication and COVID-19 Impact
  • Social Sciences › Communication › Public Relations and Crisis Communication
Keywords
misinformation, rumors, panic buying, COVID-19 pandemic, social media, protection motivation theory, social validation, viral content, threat perception, socioeconomic disparities, depression

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review