Source-linked AI summary
Why do People Share Misinformation during the COVID-19 Pandemic?
Samuli Laato, A. K. M. Najmul Islam, Muhammad Nazrul Islam, Eoin Whelan
TL;DR
COVID-19 misinformation spreads rapidly through social media, creating a need to understand why people share unverified health information and how this relates to cyberchondria. The paper develops and tests a model combining health-perception and information-load theories using survey data from 294 Facebook users in Bangladesh. Information factors predicted both outcomes, health factors predicted cyberchondria but not sharing, and cyberchondria had no direct relationship with unverified-information sharing.
Problem
The paper addresses why people share unverified COVID-19 information through social media and how misinformation sharing relates to cyberchondria during a pandemic.
Method
The study develops a model from health belief, protection-motivation, and cognitive-load theories and tests it with survey data from 294 Facebook users in Bangladesh using PLS-SEM.
Results
Information trust and overload were associated with unverified-information sharing and cyberchondria; perceived severity and susceptibility predicted cyberchondria but not sharing, and cyberchondria had no direct sharing relationship.
Takeaways & Limitations
Mitigating misinformation and cyberchondria requires healthy skepticism toward health news while guarding against information overload.
Takeaways & Limitations
The cross-sectional sample comprised university-educated social-media users in Bangladesh, limiting representativeness across the Bangladeshi or world population.
Abstract
from arXiv · showhide
The World Health Organization have emphasised that misinformation - spreading rapidly through social media - poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesizing why people share unverified COVID-19 information through social media. Our findings suggest a person's trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person's perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, however, males were more likely to share news without fact checking their source. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy skepticism of health news while simultaneously guarding against information overload.
1. Introduction
COVID-19 misinformation spreads rapidly through social media and threatens public health by undermining informed decisions and recommended behaviors. This study therefore examines individual drivers of unverified information sharing and cyberchondria using health-perception and information-load perspectives.
- Problem: Misinformation is false or inaccurate information that can threaten public health during pandemics, especially when rapidly spread through social media.It may reduce adherence to recommended measures or encourage non-recommended behaviors.
- Problem: Misinformation can fuel cyberchondria, poor health decisions, and, in severe cases, deaths caused by inaccurate evaluation of the situation.Accurate information is necessary for rational decisions and appropriate action during disruptive events.
- Problem: COVID-19 made reliable information especially important because its novelty, ambiguity, and abundance increased the risk of health anxiety.The pandemic prompted restrictions and widespread social-media sharing of COVID-19 news, including misinformation.
- Study aim: The study aims to empirically identify individual drivers of COVID-19 misinformation sharing and cyberchondria from health-perception and information-load perspectives.Prior work identified high trust in online sources as a pertinent explanation for misinformation sharing.
- Study aim: The proposed model draws on perceived susceptibility, perceived severity, information trust, and information overload to explain misinformation sharing and cyberchondria.The study tests these hypothesized relationships using survey data from Facebook users.
- Study aim: The model predicts that information factors are associated with both cyberchondria and unverified-information sharing, whereas health factors predict cyberchondria but not sharing.The study also reports no direct relationship between cyberchondria and unverified-information sharing.
2. Background
The background connects social-media misinformation and cyberchondria through prior research on dissemination mechanisms, health anxiety, and information processing. It motivates a framework combining health-belief, protection-motivation, and cognitive-load theories.
- Social Media and Misinformation Sharing: Figure 1 groups misinformation and social-media research into three broad themes, including computer-science detection and prevention and impacts on politics, science, and society.The study draws primarily from the stream examining misinformation’s broader impacts.
- Social Media and Misinformation Sharing: Social media accelerates fake-news dissemination, while bots, personal motives, ideological commitments, confirmation bias, and social influence can also affect its spread.Prior studies identify both platform-level and person-level mechanisms.
- Social Media and Misinformation Sharing: People’s onward sharing of fake news depends on content relevance, shock value, and believability rather than solely on the source.Lack of online-environment experience and resulting trust may also contribute to sharing.
- Research gap: Existing misinformation research had rarely examined sharing during a pandemic, motivating this study’s focus on misinformation drivers in the COVID-19 context.The authors note that fear and anxiety can alter decision-making during such conditions.
- Cyberchondria: Cyberchondria is repeated online health-information searching driven by underlying health worry that increases anxiety and may impair functioning.It has been associated with increased anxiety and functional impairment.
- Cyberchondria: Prior literature links cyberchondria with anxiety and information overload, while reporting inconsistent or limited relationships with demographic and medical-status variables.Continued seeking of reinforcing information is one proposed route connecting overload and cyberchondria.
- Theoretical Foundation: The study combines the health belief model, protection-motivation theory, and cognitive load theory to address health-risk and technological influences together.Perceived susceptibility and severity represent health threats, while cognitive load theory addresses limited information-processing capacity.
- Theoretical Foundation: Cognitive overload can reduce trust and self-control, making careless decisions more likely when people cannot adequately process surrounding information.This provides a rationale for examining information overload alongside health beliefs and misinformation sharing.
3. Research Model and Hypotheses
The research model proposes that online information trust and overload contribute to misinformation sharing and cyberchondria, while health perceptions may shape these outcomes. It also examines cyberchondria as a pathway to sharing unverified COVID-19 information.
- Effects of Online Information: Online information trust is hypothesized to increase both unverified COVID-19 information sharing and cyberchondria.
- Effects of Online Information: Information overload is hypothesized to increase unverified information sharing and cyberchondria by reducing people's ability to verify sources under high cognitive load.The model draws on limited working memory and the difficulty of evaluating rapidly arriving information during novel situations.
- Effects of Health Beliefs: The model proposes that perceived severity and susceptibility increase unverified COVID-19 information sharing.
- Effects of Health Beliefs: Perceived severity and susceptibility are also hypothesized to increase cyberchondria.The rationale links pandemic information about situation severity with health-related worry and online searching.
- Effects of Health Beliefs: Cyberchondria is hypothesized to increase unverified information sharing because repeated health searches expose users to many sources, including difficult-to-distinguish misinformation.
- Research Model: The proposed hypotheses are connected in a research model presented in Figure 2.
4. Methodology
The study surveyed Bangladeshi social media users and analyzed the data using PLS-SEM to test the research model. The structural model explained variance in cyberchondria and unverified information sharing, with gender effects and post hoc interaction analyses also examined.
- Data Collection: Data were collected through an online survey of Bangladeshi social media users in March 2020 using mostly validated scales.Unverified COVID-19 information sharing was measured with items developed specifically for this study.
- Sample: 294 of 299 completed responses were acceptable, and approximately 60% of respondents were male.
- Data Analysis: PLS-SEM and SmartPLS were used to assess reliability, validity, and the structural model.Reliability and validity were evaluated using thresholds from Fornell and Larcker (1981).
- Structural Model Results: The model explained 28% of variance in cyberchondria and 14% of variance in unverified information sharing.Six of the nine hypothesized relationships were supported by the data.
- Control Variables: Gender had a negative effect on unverified information sharing and a positive effect on cyberchondria, while age had no effect on either dependent variable.
- Post Hoc Analyses: Post hoc analyses found two significant interaction terms and showed that information overload reinforced the influence of cyberchondria on unverified information sharing.The significant interactions involved information overload with age and another hypothesized relationship; the overload reinforcement was significant at p<0.05.
5. Discussion
The discussion identifies information trust and overload as central correlates of unverified COVID-19 information sharing and cyberchondria, while also reporting demographic differences and boundaries for interpretation. It proposes interventions and future research focused on managing information exposure, but notes that key relationships remain contextually constrained.
- Key Findings: Trust in social media news and social media overload predicted unverified COVID-19 information sharing, whereas perceived severity and susceptibility did not.Cyberchondria itself did not influence sharing, although information overload reinforced its effect on unverified information sharing.
- Key Findings: Online information trust, information overload, perceived severity, and perceived susceptibility all increased cyberchondria, with information overload having the stronger influence.The authors report that all hypotheses predicting cyberchondria were confirmed.
- Key Findings: Females experienced more cyberchondria but were less likely than males to share unverified information on social media.The authors describe both gender effects as significant and contrast them with prior findings.
- Key Findings: Age attenuated the effects of information overload and perceived severity on both cyberchondria and unverified information sharing.The authors suggest older people therefore experience less cyberchondria and share less unverified information.
- Theoretical implications: The study links misinformation sharing and cyberchondria through shared background factors and introduces a validated construct for unverified information sharing.It combines health-behavior and instructional-science perspectives in the COVID-19 context.
- Theoretical implications: Online information trust and information overload were identified as the two main antecedents of unverified information sharing, extending prior work by quantitatively verifying overload in COVID-19.The same study also found four factors positively correlated with cyberchondria: trust, overload, perceived severity, and perceived susceptibility.
- Practical implications: The authors propose nudging users to assess sources and consume manageable amounts of content, while acknowledging that such interventions remain untested in personally involved crises.They also suggest restricting COVID-19-specific exposure could curb misinformation and cyberchondria.
- Limitations and Future Research: The cross-sectional survey of university-educated Bangladeshi social-media users may not represent the broader Bangladeshi or global population.The design also cannot account for behavioral changes during the pandemic.
Appendix
The appendix indicates that its content will be provided in the final publication.
- Appendix content will be provided in the final publication.