Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective.
Open accessAbstract
Social media plays a significant role during pandemics such as COVID-19, as it enables people to share news as well as personal experiences and viewpoints with one another in real-time, globally. Building off the affordance lens and cognitive load theory, we investigate how motivational factors and personal attributes influence social media fatigue and the sharing of unverified information during the COVID-19 pandemic. Accordingly, we develop a model which we analyse using the structural equation modelling and neural network techniques with data collected from young adults in Bangladesh (N = 433). The results show that people, who are driven by self-promotion and entertainment, and those suffering from deficient self-regulation, are more likely to share unverified information. Exploration and religiosity correlated negatively with the sharing of unverified information. However, exploration also increased social media fatigue. Our findings indicate that the different use purposes of social media introduce problematic consequences, in particular, increased misinformation sharing.
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Use of social media during the pandemic was associated with feeling overwhelmed due to information overload related to COVID-19 and had a significant impact on users' wellbeing.
“Recent studies conducted showed that the use of social media during the pandemic was associated with feeling overwhelmed due to information overload related to COVID-19, and had a significant impact on users’ wellbeing (41).”
From The Evolution of Psychological and Behavioral Consequences of Self-Isolation During Lockdown: A Longitudinal Study Across United Kingdom and Italy (2022) · cites this paper as [41]
The Introduction links abundant COVID-19 information to information overload and heightened health anxiety, and the Research model and hypotheses section explains how increased social media use may lead to overload and fatigue. The paper does not establish a significant impact of pandemic social media use on users’ overall wellbeing; that part of the claim goes beyond what it reports. The cited passages have no page numbers in our copy. (LLM, conf=0.84)
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Albahri, A. S., Alnoor, A., Zaidan, A. A., Albahri, O. S., Hameed, H., Zaidan, B. B., Peh, S. S., Zain, A. B., Siraj, S. B., Masnan, A. H. B., et al. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00503-w · Cited by 173 · see citing works
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Zaninotto, F., Bossi, F., Terry, P., Riccaboni, M., & Galli, G. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.826277 · Cited by 7 · see citing works
“(41) that a negative effect of social media can occur due to the overwhelming amount of information that can be associated with it.”
Background
- Authors
- Islam, A. K. M. N., Laato, S., Talukder, S., & Sutinen, E.
- Affiliations
- University of Turku (FI)
- Corresponding author
- Islam, A.K.M. Najmul
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 159
- Pages
- 120201
- ISSN
- 0040-1625 (ISSN-L)
- PMC
- PMC7354273
- OpenAlex
- W3042114636
- Cited by
- 538
- References
- 106
- Citations by year
- 2026: 23 · 2025: 65 · 2024: 88 · 2023: 125 · 2022: 114 · 2021: 108 · 2020: 15
- FWCI
- 121.06
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Technological Forecasting and Social Change (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UTUPub (University of Turku) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- LUTPub (LUT University) (submittedVersion, cc-by-nc-nd)
- 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 sharing, social media fatigue, COVID-19 pandemic, affordance theory, cognitive load theory, unverified information, entertainment motivation, self-regulation, exploration, religiosity, young adults, motivations for social media use
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 ›
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