Source Credibility Labels and Other Nudging Interventions in the Context of Online Health Misinformation: A Systematic Literature Review
Abstract
:1. Introduction
1.1. The Spread of Online Health Misinformation
1.2. Nudging
1.3. Labeling Source Credibility in a Health Context
2. Materials and Methods
2.1. Search Strategy
- By referring to the “treatment”: the “source credibility labels”;
- By referring to the “disease”: “misinformation”;
- By limiting it to the relevant context: “online information”.
2.2. Inclusion Criteria
The Inclusion of Similar Nudging Interventions
2.3. Exclusion Criteria
2.4. Assessment of Risk of Bias
3. Results
4. Discussion
4.1. Findings
4.2. Future Research
- Examining different types of credibility labels: Future research should explore the impact of some of the source credibility labels currently in existence to understand their unique impacts on online health information consumption. It would also be useful to understand how impact changes from credibility labeling schemes such as PIF Tick (which communicates only positive credibility) to Healthguard (which communicates both positive and negative credibility) to warning labels such as those found in digital platforms (which communicate only negative credibility).
- Examining source perception: It would be useful to understand how incorporating a reference to the nature of the source (e.g., journalistic, governmental, health institutions, health experts, etc.) impacts decision-making in source selection, source confidence, and decision to share content from sources.
- Examining labeler perception: Similarly, it would be interesting to understand if audiences react differently to labels depending on who assessed the source and issued the label (e.g., doctors, journalists, health institutions, government, artificial intelligence, etc.).
- Comparative studies across different health topics: Research could compare the effectiveness of interventions across various health topics, including those that are highly polarized like vaccination and COVID-19, to those that might be less controversial in order to understand how polarization contaminates perception and impact.
- Impact on behavior change: Future research should aim to measure not just belief changes but also whether these interventions lead to actual behavior change, like intent to share.
- Cross-demographic studies: Considering the cultural context in the acceptance of health information, studies should examine how these interventions work across different cultures and regions, different ages and education levels, and different socioeconomic levels.
- Cross-environment studies: Since labeling can be applied in multiple contexts, it would be interesting to develop research comparing how its impact may vary depending on the environment (e.g., how the same approach compares when applied to X, Facebook, Instagram, TikTok, and Google search results; and how different environments may benefit from certain approaches versus others).
- Label design studies: Different labeling designs have been applied over time, and creativity may unlock different manners of conveying information on source credibility when applied to online health information. Designs such as ribbons, seals, stamps, marks, ticks, non-textual and textual, color-coded or not, numerical, etc., may yield different results that should be assessed.
- Integration with social media platforms: Researching the collaboration between health organizations and social media platforms could yield insights into how to effectively implement these labels in the places where people most often encounter health misinformation.
- Effectiveness of different intervention combinations: Exploring how different combinations of nudging interventions work together could provide a more nuanced understanding of how to combat online health misinformation.
- Public perception and trust in labels: Future research could also focus on how the public perceives these credibility labels and interventions and how trust in these labels can be built over time in order to increase its impact.
- Role of fact-checking organizations: Understanding how interventions can be supported or enhanced by fact-checking organizations and the impact of their endorsement on public trust and information assessment.
- Technology and algorithm influence: Examining how technology and algorithms can be optimized to support the visibility and effectiveness of these labels, including the role of artificial intelligence in flagging misinformation.
- The fast-paced nature of online platforms and online behaviors, which can quickly render interventions obsolete;
- The difficulty in designing interventions that are effective across diverse demographic groups without inadvertently amplifying misinformation;
- The challenge of measuring the real-world impact of online interventions on health outcomes, which requires complex, interdisciplinary approaches;
- The legitimacy issue of the “labeler” and how to create frameworks that ensure that credibility assessments are underpinned in objective and determinable criteria;
- The advent of artificial intelligence, how it will shift the paradigm around the creation of info- and misinformation, and how it can be leveraged to both prevent and spread online health misinformation;
- The potential for resistance from users who perceive credibility labels and interventions as forms of censorship or bias, which possibly reduced their effectiveness;
- Finally, the technical and ethical considerations in implementing these interventions including scalability concerns and the need for transparency and accountability in how information is labeled and moderated.
4.3. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- Source, origin, author, publisher, creator, influencer.
- Credibility, trustworthiness, reliability, validity, believability, accuracy, reputation, expertise, credible, trust, reliable, valid, believable, accurate, reputable, expert.
- Label, rating, certification, tick, evaluation, assessment, scheme, trustmark, mark, seal, endorsement, attestation, verified, grade, ranking, standard, badge.
Appendix B
Studies: | Is It Clear in the Study What Is the “Cause” and What Is the “Effect” (i.e., There Is No Confusion about Which Variable Comes First)? | Were the Participants Included in Any Comparisons Similar? | Were the Participants Included in any Comparisons Receiving Similar Treatment/Care, Other than the Exposure or Intervention of Interest? | Was There a Control Group? | Were There Multiple Measurements of the Outcome Both Pre and Post the Intervention/Exposure? | Was Follow Up Complete and If Not, Were Differences between Groups in Terms of Their Follow Up Adequately Described and Analyzed? | Were the Outcomes of Participants Included in any Comparisons Measured in the Same Way? | Were Outcomes Measured in a Reliable Way? | Was Appropriate Statistical Analysis Used? | Total Number of Yes % | Risk of Bias * | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Bates et al. 2007) | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | 77.78% | Low | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Barker et al. 2010) | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | N/A | N/A | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Westerwick et al. 2017) | Yes | Yes | Yes | No | Yes | No | Yes | Yes | Yes | 77.78% | Low | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Bea-Muñoz et al. 2016) | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable | N/A | N/A | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Jongenelis et al. 2018) | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | 88.89% | Low | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Borah and ** review. International Journal of Disaster Risk Reduction 78: 103144. [Google Scholar] [CrossRef] Figure 1.
PRISMA flow chart.
Table 1.
Summary of interventions, results, and environments.
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and CiteMDPI and ACS Style
Marecos, J.; Tude Graça, D.; Goiana-da-Silva, F.; Ashrafian, H.; Darzi, A. Source Credibility Labels and Other Nudging Interventions in the Context of Online Health Misinformation: A Systematic Literature Review. Journal. Media 2024, 5, 702-717. https://doi.org/10.3390/journalmedia5020046
AMA Style
Marecos J, Tude Graça D, Goiana-da-Silva F, Ashrafian H, Darzi A. Source Credibility Labels and Other Nudging Interventions in the Context of Online Health Misinformation: A Systematic Literature Review. Journalism and Media. 2024; 5(2):702-717. https://doi.org/10.3390/journalmedia5020046 Chicago/Turabian StyleMarecos, Joao, Duarte Tude Graça, Francisco Goiana-da-Silva, Hutan Ashrafian, and Ara Darzi. 2024. "Source Credibility Labels and Other Nudging Interventions in the Context of Online Health Misinformation: A Systematic Literature Review" Journalism and Media 5, no. 2: 702-717. https://doi.org/10.3390/journalmedia5020046 Article Metrics |