» Articles » PMID: 27255736

Manipulating Google's Knowledge Graph Box to Counter Biased Information Processing During an Online Search on Vaccination: Application of a Technological Debiasing Strategy

Overview
Publisher JMIR Publications
Date 2016 Jun 4
PMID 27255736
Citations 12
Authors
Affiliations
Soon will be listed here.
Abstract

Background: One of people's major motives for going online is the search for health-related information. Most consumers start their search with a general search engine but are unaware of the fact that its sorting and ranking criteria do not mirror information quality. This misconception can lead to distorted search outcomes, especially when the information processing is characterized by heuristic principles and resulting cognitive biases instead of a systematic elaboration. As vaccination opponents are vocal on the Web, the chance of encountering their non‒evidence-based views on immunization is high. Therefore, biased information processing in this context can cause subsequent impaired judgment and decision making. A technological debiasing strategy could counter this by changing people's search environment.

Objective: This study aims at testing a technological debiasing strategy to reduce the negative effects of biased information processing when using a general search engine on people's vaccination-related knowledge and attitudes. This strategy is to manipulate the content of Google's knowledge graph box, which is integrated in the search interface and provides basic information about the search topic.

Methods: A full 3x2 factorial, posttest-only design was employed with availability of basic factual information (comprehensible vs hardly comprehensible vs not present) as the first factor and a warning message as the second factor of experimental manipulation. Outcome variables were the evaluation of the knowledge graph box, vaccination-related knowledge, as well as beliefs and attitudes toward vaccination, as represented by three latent variables emerged from an exploratory factor analysis.

Results: Two-way analysis of variance revealed a significant main effect of availability of basic information in the knowledge graph box on participants' vaccination knowledge scores (F2,273=4.86, P=.01), skepticism/fear of vaccination side effects (F2,273=3.5, P=.03), and perceived information quality (F2,273=3.73, P=.02). More specifically, respondents receiving comprehensible information appeared to be more knowledgeable, less skeptical of vaccination, and more critical of information quality compared to participants exposed to hardly comprehensible information. Although, there was no significant interaction effect between the availability of information and the presence of the warning, there was a dominant pattern in which the presence of the warning appeared to have a positive influence on the group receiving comprehensible information while the opposite was true for the groups exposed to hardly comprehensible information and no information at all. Participants evaluated the knowledge graph box as moderately to highly useful, with no significant differences among the experimental groups.

Conclusion: Overall, the results suggest that comprehensible information in the knowledge graph box positively affects participants' vaccination-related knowledge and attitudes. A small change in the content retrieval procedure currently used by Google could already make a valuable difference in the pursuit of an unbiased online information search. Further research is needed to gain insights into the knowledge graph box's entire potential.

Citing Articles

Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions.

Paradise Vit A, Magid A J Med Internet Res. 2024; 26:e55151.

PMID: 39120928 PMC: 11344190. DOI: 10.2196/55151.


Can biased search results change people's opinions about anything at all? a close replication of the Search Engine Manipulation Effect (SEME).

Epstein R, Li J PLoS One. 2024; 19(3):e0300727.

PMID: 38530851 PMC: 10965084. DOI: 10.1371/journal.pone.0300727.


Behavioral interventions for vaccination uptake: A systematic review and meta-analysis.

Malik A, Ahmed N, Shafiq M, Elharake J, James E, Nyhan K Health Policy. 2023; 137:104894.

PMID: 37714082 PMC: 10885629. DOI: 10.1016/j.healthpol.2023.104894.


What would happen if twitter sent consequential messages to only a strategically important subset of users? A quantification of the Targeted Messaging Effect (TME).

Epstein R, Tyagi C, Wang H PLoS One. 2023; 18(7):e0284495.

PMID: 37498911 PMC: 10374154. DOI: 10.1371/journal.pone.0284495.


The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data.

Abroms L, Yom-Tov E JMIR Infodemiology. 2023; 2(2):e37286.

PMID: 37113445 PMC: 9987180. DOI: 10.2196/37286.


References
1.
Eysenbach G, Kohler C . How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. BMJ. 2002; 324(7337):573-7. PMC: 78994. DOI: 10.1136/bmj.324.7337.573. View

2.
Kata A . A postmodern Pandora's box: anti-vaccination misinformation on the Internet. Vaccine. 2010; 28(7):1709-16. DOI: 10.1016/j.vaccine.2009.12.022. View

3.
Aczel B, Bago B, Szollosi A, Foldes A, Lukacs B . Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique. Front Psychol. 2015; 6:1120. PMC: 4523707. DOI: 10.3389/fpsyg.2015.01120. View

4.
Bonanni P . Demographic impact of vaccination: a review. Vaccine. 1999; 17 Suppl 3:S120-5. DOI: 10.1016/s0264-410x(99)00306-0. View

5.
Tversky A, Kahneman D . Judgment under Uncertainty: Heuristics and Biases. Science. 1974; 185(4157):1124-31. DOI: 10.1126/science.185.4157.1124. View