Advantages of query biased summaries in information retrieval
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The anatomy of a large-scale hypertextual Web search engine
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Evaluating implicit measures to improve web search
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What are you looking for?: an eye-tracking study of information usage in web search
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The influence of caption features on clickthrough patterns in web search
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Proceedings of the fifth ACM international conference on Web search and data mining
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Text selections as implicit relevance feedback
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 21st ACM international conference on Information and knowledge management
The presentation of health-related search results and its impact on negative emotional outcomes
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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People frequently turn to the Web with the goal of diagnosing medical symptoms. Studies have shown that diagnostic search can often lead to anxiety about the possibility that symptoms are explained by the presence of rare, serious medical disorders, rather than far more common benign syndromes. We study the influence of the appearance of potentially-alarming content, such as severe illnesses or serious treatment options associated with the queried for symptoms, in captions comprising titles, snippets, and URLs. We explore whether users are drawn to results with potentially-alarming caption content, and if so, the implications of such attraction for the design of search engines. We specifically study the influence of the content of search result captions shown in response to symptom searches on search-result click-through behavior. We show that users are significantly more likely to examine and click on captions containing potentially-alarming medical terminology such as “heart attack” or “medical emergency” independent of result rank position and well-known positional biases in users' search examination behaviors. The findings provide insights about the possible effects of displaying implicit correlates of searchers' goals in search-result captions, such as unexpressed concerns and fears. As an illustration of the potential utility of these results, we developed and evaluated an enhanced click prediction model that incorporates potentially-alarming caption features and show that it significantly outperforms models that ignore caption content. Beyond providing additional understanding of the effects of Web content on medical concerns, the methods and findings have implications for search engine design. As part of our discussion on the implications of this research, we propose procedures for generating more representative captions that may be less likely to cause alarm, as well as methods for learning to more appropriately rank search results from logged search behavior, for examples, by also considering the presence of potentially-alarming content in the captions that motivate observed clicks and down-weighting clicks seemingly driven by searchers' health anxieties.