Daniel J. Liebling

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As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We(More)
We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or(More)
This paper presents an algorithm that predicts with very high accuracy which Web search result a user will click for one sixth of all Web queries. Prediction is done via a straightforward form of personalization that takes advantage of the fact that people often use search engines to re-find previously viewed resources. In our approach, an individual's past(More)
In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries,(More)
Understanding how users examine result pages across a broad range of information needs is critical for search engine design. Cursor movements can be used to estimate visual attention on search engine results page (SERP) components, including traditional snippets, aggregated results, and advertisements. However, these signals can only be leveraged for SERPs(More)
People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the replies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “friendsourced” answers with crowdsourced answers. By analyzing(More)
Many search engines identify bursts of activity around particular topics and reflect these back to users as Popular Now or Hot Searches. Activity around these topics typically evolves quickly in real-time during the course of a trending event. Users’ informational needs when searching for such topics will vary depending on the stage at which they engage(More)
Web search engines now offer more than ranked results. Queries on topics like weather, definitions, and movies may return inline results called answers that can resolve a searcher's information need without any additional interaction. Despite the usefulness of answers, they are limited to popular needs because each answer type is manually authored. To(More)
Although people receive trusted, personalized recommendations and auxiliary social benefits when they ask questions of their friends, using a search engine is often a more effective way to find an answer. Attempts to integrate social and algorithmic search have thus far focused on bringing social content into algorithmic search results. However, more of the(More)
Interest in and development of gesture interfaces has recently exploded, fueled in part by the release of Microsoft Corporation's Kinect, a low-cost, consumer-packaged depth camera with integrated skeleton tracking. Depth-camera-based gestures can facilitate interaction with the Web on keyboard-and-mouse-free and/or multi-user technologies, such as large(More)