Abstract
Searching for health information is one of the most popular activities on the Web. In this domain, users frequently encounter difficulties in query formulation, either because they lack knowledge of the proper medical terms or because they misspell them. To overcome these difficulties and attempt to retrieve higher-quality content, we developed a query suggestion system that provides alternative queries combining the users’ native language and English language with lay and medico-scientific terminology. To assess how the language and terminology impact the use of suggestions, we conducted a user study with 40 subjects considering their English proficiency, health literacy and topic familiarity. Results show that suggestions are used most often at the beginning of search sessions. English suggestions tend to be preferred to the ones formulated in the users’ native language, at all levels of English proficiency. Medico-scientific suggestions tend to be preferred to lay suggestions at higher levels of health literacy.
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Acknowledgments
This work was partially funded by project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016”, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
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Lopes, C.T., Ribeiro, C. (2016). Effects of Language and Terminology on the Usage of Health Query Suggestions. In: Fuhr, N., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2016. Lecture Notes in Computer Science(), vol 9822. Springer, Cham. https://doi.org/10.1007/978-3-319-44564-9_7
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