6 research outputs found

    Studying How Health Literacy Influences Attention during Online Information Seeking

    Get PDF
    Health literacy affects how people understand health information and, therefore, should be considered by search engines in health searches. In this work, we analyze how the level of health literacy is related to the eye movements of users searching the web for health information. We performed a user study with 30 participants that were asked to search online in the context of three work task situations defined by the authors. Their eye interactions with the Search Results Page and the Result Pages were logged using an eye-tracker and later analyzed. When searching online for health information, people with adequate health literacy spend more time and have more fixations on Search Result Pages. In this type of page, they also pay more attention to the results' hyperlink and snippet and click in more results too. In Result Pages, adequate health literacy users spend more time analyzing textual content than people with lower health literacy. We found statistical differences in terms of clicks, fixations, and time spent that could be used as a starting point for further research. That we know of, this is the first work to use an eye-tracker to explore how users with different health literacy search online for health-related information. As traditional instruments are too intrusive to be used by search engines, an automatic prediction of health literacy would be very useful for this type of system

    Implicit relevance feedback from eye movements

    No full text
    Abstract. We explore the use of eye movements as a source of implicit relevance feedback information. We construct a controlled information retrieval experiment where the relevance of each text is known, and test usefulness of implicit relevance feedback with it. If perceived relevance of a text can be predicted from eye movements, eye movement signal must contain information on the relevance. The result is that relevance can be predicted to a considerable extent with discriminative hidden Markov models, and clearly better than randomly already with simple linear models of time-averaged data.
    corecore