Captions and biases in diagnostic search
Abstract
People frequently turn to the Web with the goal of diagnosing medical symptoms. Studies have shown that diagnostic search can often lead to anxiety about the possibility that symptoms are explained by the presence of rare, serious medical disorders, rather than far more common benign syndromes. We study the influence of the appearance of potentially-alarming content, such as severe illnesses or serious treatment options associated with the queried for symptoms, in captions comprising titles, snippets, and URLs. We explore whether users are drawn to results with potentially-alarming caption content, and if so, the implications of such attraction for the design of search engines. We specifically study the influence of the content of search result captions shown in response to symptom searches on search-result click-through behavior. We show that users are significantly more likely to examine and click on captions containing potentially-alarming medical terminology such as “heart attack” or “medical emergency” independent of result rank position and well-known positional biases in users' search examination behaviors. The findings provide insights about the possible effects of displaying implicit correlates of searchers' goals in search-result captions, such as unexpressed concerns and fears. As an illustration of the potential utility of these results, we developed and evaluated an enhanced click prediction model that incorporates potentially-alarming caption features and show that it significantly outperforms models that ignore caption content. Beyond providing additional understanding of the effects of Web content on medical concerns, the methods and findings have implications for search engine design. As part of our discussion on the implications of this research, we propose procedures for generating more representative captions that may be less likely to cause alarm, as well as methods for learning to more appropriately rank search results from logged search behavior, for examples, by also considering the presence of potentially-alarming content in the captions that motivate observed clicks and down-weighting clicks seemingly driven by searchers' health anxieties.
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Potentially alarming content in captions, snippets, and URLs of search results influences search-result click-through behavior so that this attribute makes them more likely to be selected.
“In a consecutive study, White and Horvitz [34] showed that potentially alarming content (eg, heart attack and medical emergency) in captions, snippets, and URLs of search results influences the search-result click-through behavior so that this attribute makes them more likely to be selected.”
From Conceptualizations of cyberchondria and relations to the anxiety spectrum: Systematic review and meta-analysis (2021) · cites this paper as [34]
Current and partly inhomogeneous results raise doubts about factors that amplify anxiety during health-related internet use.
“However, current and partly inhomogeneous results raise doubts in this context [13,34,36].”
From Conceptualizations of cyberchondria and relations to the anxiety spectrum: Systematic review and meta-analysis (2021) · cites this paper as [34]
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- Authors
- White, R. W., & Horvitz, E.
- Affiliations
- Microsoft (United States) (US)
- Venue
- ACM Transactions on the Web
- Published
- 2013-10
- Volume
- 7
- Issue
- 4
- Pages
- 1-28
- ISSN
- 1559-1131 (ISSN-L)
- 1559-114X
- OpenAlex
- W2171664132
- Cited by
- 29
- References
- 63
- Citations by year
- 2025: 1 · 2024: 5 · 2022: 3 · 2021: 4 · 2020: 5 · 2019: 1 · 2017: 2 · 2016: 3 · 2015: 2 · 2014: 3
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on the Web (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- symptom-based diagnosis, click-through behavior, search engine design, health anxiety, search result ranking, position bias
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.
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