Users frequently modify a previous search query in hope of retrieving better results. These modifications are called query reformulations or query refinements. Existing research has studied how web search engines can propose reformulations, but has given less attention to how people perform query reformulations. In this paper, we aim to better understand how web searchers refine queries and form a theoretical foundation for query reformulation. We study users' reformulation strategies in the context of the AOL query logs. We create a taxonomy of query refinement strategies and build a high precision rule-based classifier to detect each type of reformulation. Effectiveness of reformulations is measured using user click behavior. Most reformulation strategies result in some benefit to the user. Certain strategies like add/remove words, word substitution, acronym expansion, and spelling correction are more likely to cause clicks, especially on higher ranked results. In contrast, users often click the same result as their previous query or select no results when forming acronyms and reordering words. Perhaps the most surprising finding is that some reformulations are better suited to helping users when the current results are already fruitful, while other reformulations are more effective when the results are lacking. Our findings inform the design of applications that can assist searchers; examples are described in this paper.

Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.

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We provide the first solid evidence that Chinese superstitious beliefs can have significant effects on house prices in a North American market with a large immigrant population. Using real estate data on close to 117,000 house sales, we find that houses with address number ending in “4” are sold at a 2.2% discount and those ending in “8” are sold at a 2.5% premium in comparison to houses with other addresses. These price effects are found either in neighborhoods with a higher than average percentage of Chinese residents, consistent with cultural preferences, or in repeated transactions, consistent with speculative behavior. (JEL D03, R2, Z1)

Understanding the impact of individual and task differences on search result page examination strategies is important in developing improved search engines. Characterizing these effects using query and click data alone is common but insufficient since they provide an incomplete picture of result examination behavior. Cursor-or gaze-tracking studies reveal richer interaction patterns but are often done in small-scale laboratory settings. In this paper we leverage large-scale rich behavioral log data in a naturalistic setting. We examine queries, clicks, cursor movements, scrolling, and text highlighting for millions of queries on the Bing commercial search engine to better understand the impact of user, task, and user-task interactions on user behavior on search result pages (SERPs). By clustering users based on cursor features, we identify individual, task, and user-task differences in how users examine results which are similar to those observed in small-scale studies. Our findings have implications for developing search support for behaviorally-similar searcher cohorts, modeling search behavior, and designing search systems that leverage implicit feedback.

Existing sleep-tracking apps and devices provide simple descriptive statistics or generic recommendations for everyone. In this work, we aim to leverage cohort-based sleep data to provide recommendations to improve an individual's sleep. We report a 4-week study (N = 39) conducted to compare three alternatives: 1) no recommendation, 2) general recommendation, and 3) cohort-based recommendation, using six sleep quality metrics. For the cohort-based recommendation, recommendations were generated based on "similar users" using about 40 million sleep events from Microsoft Band users. Our results indicate that cohort-based systems for health recommendations can prompt a desire for behavior change inspired by social comparison and increased awareness about sleep habits. However, in order to be effective, such systems need to establish their credibility and to be able to generate cohorts based on features that are important to users. Finally, we provide further suggestions and design implications for future cohort-based recommendation systems for healthy sleep.