dc.contributor.authorPian, Wenjing
dc.contributor.authorKhoo, Christopher Soo Guan
dc.contributor.authorChi, Jianxing
dc.date.accessioned2018-08-29T08:11:05Z
dc.date.available2018-08-29T08:11:05Z
dc.date.issued2017
dc.identifier.citationPian, W., Khoo, C. S. G., & Chi, J. (2017). Automatic classification of users’ health information need context : logistic regression analysis of mouse-click and eye-tracker data. Journal of Medical Internet Research, 19(12), e424-. doi:10.2196/jmir.8354en_US
dc.identifier.issn1439-4456en_US
dc.identifier.urihttp://hdl.handle.net/10220/45739
dc.description.abstractBackground: Users searching for health information on the Internet may be searching for their own health issue, searching for someone else’s health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. Objective: The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. Methods: A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user’s mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. Results: An analysis of variance (ANOVA) analysis found that users’ browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user’s type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users’ age, education level, and the urgency of their information need. Conclusions: A user’s type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function.en_US
dc.format.extent16 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesJournal of Medical Internet Researchen_US
dc.rights© 2017 Wenjing Pian, Christopher SG Khoo, Jianxing Chi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.12.2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.en_US
dc.subjectInformation-seeking Behavioren_US
dc.subjectSocial Mediaen_US
dc.subjectDRNTU::Library and information science::Librariesen_US
dc.titleAutomatic classification of users’ health information need context : logistic regression analysis of mouse-click and eye-tracker dataen_US
dc.typeJournal Article
dc.contributor.schoolWee Kim Wee School of Communication and Informationen_US
dc.identifier.doihttp://dx.doi.org/10.2196/jmir.8354
dc.description.versionPublished versionen_US


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