Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Enhancing digital libraries using missing content analysis
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Geographic features in web search retrieval
Proceedings of the 2nd international workshop on Geographic information retrieval
Discovering and using groups to improve personalized search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Describing and predicting information-seeking behavior on the Web
Journal of the American Society for Information Science and Technology
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Science Computer Review
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
A case study of using geographic cues to predict query news intent
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
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The information need of users and the documents which answer this need are frequently contingent on the different characteristics of users. This is especially evident during natural disasters, such as earthquakes and violent weather incidents, which create a strong transient information need. In this article, we investigate how the information need of users, as expressed by their queries, is affected by their physical detachment, as estimated by their physical location in relation to that of the event, and by their social detachment, as quantified by the number of their acquaintances who may be affected by the event. Drawing on large-scale data from ten major events, we show that social and physical detachment levels of users are a major influence on their search engine queries. We demonstrate how knowing social and physical detachment levels can assist in improving retrieval for two applications: identifying search queries related to events and ranking results in response to event-related queries. We find that the average precision in identifying relevant search queries improves by approximately 18%, and that the average precision of ranking that uses detachment information improves by 10%. Using both types of detachment achieved a larger gain in performance than each of them separately.