research-article

How users search and what they search for in the medical domain: Understanding laypeople and experts through query logs

Authors:

Henning Müller,

Charles E. Kahn, Jr.Authors Info & Claims

Information Retrieval Journal, Volume 19, Issue 1-2

Pages 189 - 224

https://doi.org/10.1007/s10791-015-9269-8

Published: 01 April 2016 Publication History

Abstract

The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America Online, Health on the Net, Turning Research Into Practice and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifier to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifier built, could be used to detect specific user groups and then adapt search results to the user group.

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How users search and what they search for in the medical domain: Understanding laypeople and experts through query logs

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Published In

cover image Information Retrieval

Information Retrieval Volume 19, Issue 1-2

Apr 2016

224 pages

ISSN:1386-4564

Issue’s Table of Contents

© Springer Science+Business Media New York 2015.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2016

Accepted: 19 September 2015

Received: 31 December 2014

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Cited By

Reimer JSchmidt SFröbe MGienapp LScells HStein BHagen MPotthast MChen HDuh WHuang HKato MMothe JPoblete B(2023)The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web ArchivesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591890(2848-2860)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591890
Tamine LGoeuriot L(2021)Semantic Information Retrieval on Medical TextsACM Computing Surveys10.1145/346247654:7(1-38)Online publication date: 17-Sep-2021
https://dl.acm.org/doi/10.1145/3462476
Petras VLüschow ARamthun RStiller JEspaña-Bonet CHenning S(2020)Query or Document Translation for Academic Search – What’s the Real Difference?Experimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-030-58219-7_3(28-42)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-58219-7_3
Liu JZhang X(2019)The role of domain knowledge in document selection from search resultsJournal of the Association for Information Science and Technology10.1002/asi.2419970:11(1236-1247)Online publication date: 6-Oct-2019
https://dl.acm.org/doi/10.1002/asi.24199
Zuccon GKoopman BCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)SIGIR 2018 Tutorial on Health Search (HS2018)The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210188(1391-1394)Online publication date: 27-Jun-2018
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Soldaini LYom-Tov EBarrett RCummings RAgichtein EGabrilovich E(2017)Inferring Individual Attributes from Search Engine Queries and Auxiliary InformationProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052629(293-301)Online publication date: 3-Apr-2017
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Karanam SJorge-Botana GOlmos ROostendorp H(2017)The role of domain knowledge in cognitive modeling of information searchInformation Retrieval10.1007/s10791-017-9308-820:5(456-479)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10791-017-9308-8
Palotti JPerego RSebastiani FAslam JRuthven IZobel J(2016)Beyond Topical RelevanceProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911480(1167-1167)Online publication date: 7-Jul-2016
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