Characterizing the influence of domain expertise on web search behavior
Abstract
Domain experts search differently than people with little or no domain knowledge. Previous research suggests that domain experts employ different search strategies and are more successful in finding what they are looking for than non-experts. In this paper we present a large-scale, longitudinal, log-based analysis of the effect of domain expertise on web search behavior in four different domains (medicine, finance, law, and computer science). We characterize the nature of the queries, search sessions, web sites visited, and search success for users identified as experts and non-experts within these domains. Large-scale analysis of real-world interactions allows us to understand how expertise relates to vocabulary, resource use, and search task under more realistic search conditions than has been possible in previous small-scale studies. Building upon our analysis we develop a model to predict expertise based on search behavior, and describe how knowledge about domain expertise can be used to present better results and query suggestions to users and to help non-experts gain expertise.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There are known differences in how non-experts and experts interact with and validate systems outputs, particularly for critical application domains such as health.
“This is problematic as there are known differences in how non-experts and experts interact with and validate systems outputs (White et al., 2009; Patton et al., 2019), particularly for critical application domains such as health.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as White et al., 2009
Cited by
Sorted by citations
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“This is problematic as there are known differences in how non-experts and experts interact with and validate systems outputs (White et al., 2009; Patton et al., 2019), particularly for critical application domains such as health.”
BackgroundAugmenting web pages and search results to support credibility assessment
Schwarz, J., & Morris, M. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979127 · Cited by 181 · see citing works
Captions and biases in diagnostic search
White, R. W., & Horvitz, E. (2013) · ACM Transactions on the Web · doi:10.1145/2486040 · Cited by 29 · see citing works
Self-citationAqle, A., Al-Thani, D., & Jaoua, A. (2022) · Universal Access in the Information Society · doi:10.1007/s10209-020-00777-w · Cited by 16 · see citing works
- Authors
- White, R. W., Dumais, S. T., & Teevan, J.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Proceedings of the Second ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2009-02-09
- Pages
- 132-141
- OpenAlex
- W2140619391
- Cited by
- 312
- References
- 0
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 13 · 2023: 14 · 2022: 14 · 2021: 19 · 2020: 26 · 2019: 13 · 2018: 20 · 2017: 17 · 2016: 17 · 2015: 25 · 2014: 34 · 2013: 20 · 2012: 25
- FWCI
- 16.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- domain knowledge, online search behavior, search success, search strategies, search sessions, query suggestion, non-expert users
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 ›
Are you an author, or do you see a problem with this page? Request a review
