The effects of expertise and feedback on search term selection and subsequent learning

Hembrooke, Helene; Granka, Laura; Gay, Geraldine; Liddy, Elizabeth D.

doi:10.1002/asi.20180

Cited by 77 publications

(78 citation statements)

References 30 publications

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“…29 It was also found that with an increase in the level of domain knowledge, users tend to use more terms in queries; domain experts use more effective strategy, conduct more complex searches, and incorporate more unique terms. 30 Many studies considered how representatives of a specific domain or discipline search for information. Studies have shown that scientists' searching is usually aimed at specific questions or problems that they face when conducting an experiment, writing up results, or checking the accuracy of information in hand.…”

Section: Domain Knowledge and User Searchingmentioning

confidence: 99%

Understanding the Information Needs of Large-Scale Digital Library Users

Zavalina¹,

Vassilieva²

2014

LRTS

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A growing number of large-scale digital libraries, portals that aggregate millions of digitized or born-digital items of historical, cultural, or educational value that are organized into digital collections, have been developed in recent decades. While many of these large-scale digital libraries have been created for the general public, some serve more specific audiences of scholars and educators in different disciplines or domains, for example, history, science, technology, engineering, and mathematics (STEM), etc.To improve user interaction with large-scale digital libraries and to make sure they successfully meet their users' information needs, the design and development of large-scale digital libraries' discovery and access systems should be informed by general user tasks such as finding, identifying, selecting, and obtaining information as well as by the needs and information-searching patterns of their specific intended user communities.1 Various groups of users (e.g., researchers, educators, and enthusiasts) may use digital libraries differently because of their varying information needs; moreover, users' information-searching strategies may differ in the large-scale digital libraries that function in distinct domains, or subject areas. These differences may require specific policies regarding the organization and description of information objects in large-scale digital libraries.The extensive digitization and organization of large-scale digital libraries require in-depth research of current trends in use of these emerging and rapidly developing resources. However, systematic investigation into the user searching Dr. Oksana Zavalina (oksana.zavalina @unt

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Section: Domain Knowledge and User Searchingmentioning

confidence: 99%

Understanding the Information Needs of Large-Scale Digital Library Users

Zavalina¹,

Vassilieva²

2014

LRTS

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“…For health consumers, a fundamental poverty in the mental representation of the medical domain underlies issues with terminology, and it is likely to affect three stages of information retrieval outlined by Keselman and her colleagues [43]: (i) the formation of a theory or hypothesis based on background knowledge, (ii) generation of a search goal, and (iii) evaluation of search results. Domain knowledge influences search strategies and the ability to benefit from help tools [44][45][46][47]. Therefore, the success of efforts aimed at CHV is likely to be limited without mechanisms for helping the consumer form a richer mental representation of the medical or health issue for which they seek information.…”

Section: Obstacles For Chv and Nlpmentioning

confidence: 99%

Overcoming the linguistic divide: a barrier to consumer health information

Boden¹

2014

J Can Health Libr Assoc

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Seeking health information online has become very popular. Despite this popularity, health consumers face many barriers to successfully retrieving good quality health information. This paper reviews the literature on the linguistic divide between health consumers and consumer health information. Consumer health vocabularies (CHV) and natural language processing (NLP) show potential for bridging the divide, thereby improving recall and precision from information retrieval systems. Developers of digital libraries can incorporate CHV and (or) NLP as help tools to facilitate health consumers' search success. Deeper issues, such as health consumers' mental representation of medical domain, must also be addressed in future research for optimal benefit from such help tools.Boden 80

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“…However, a lot more can be done. For example, word choices might reveal the age of the user [31] or their level of domain expertise [15], which in turn would have implications for the medical meaning of symptoms. We also have not yet considered formulation nuances of queries other than the landmark query, which might hold much richer information.…”

Section: Discussionmentioning

confidence: 99%

Interactions between health searchers and search engines

Schoenherr

White

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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The Web is an important resource for understanding and diagnosing medical conditions. Based on exposure to online content, people may develop undue health concerns, believing that common and benign symptoms are explained by serious illnesses. In this paper, we investigate potential strategies to mine queries and searcher histories for clues that could help search engines choose the most appropriate information to present in response to exploratory medical queries. To do this, we performed a longitudinal study of health search behavior using the logs of a popular search engine. We found that query variations which might appear innocuous (e.g. "bad headache" vs "severe headache") may hold valuable information about the searcher which could be used by search engines to improve performance. Furthermore, we investigated how medically concerned users respond differently to search engine result pages (SERPs) and find that their disposition for clicking on concerning pages is pronounced, potentially leading to a self-reinforcement of concern. Finally, we studied to which degree variations in the SERP impact future search and real-world healthseeking behavior and obtained some surprising results (e.g., viewing concerning pages may lead to a short-term reduction of real-world health seeking).

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The effects of expertise and feedback on search term selection and subsequent learning

Cited by 77 publications

References 30 publications

Understanding the Information Needs of Large-Scale Digital Library Users

Understanding the Information Needs of Large-Scale Digital Library Users

Overcoming the linguistic divide: a barrier to consumer health information

Interactions between health searchers and search engines

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