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The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas. [ABSTRACT FROM AUTHOR]

Session characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: “hit and run” sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from “hit and run” sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help. [ABSTRACT FROM AUTHOR]

Two large data sets of queries submitted to the Excite search service were used to derive different distributions of query and search behaviours (query-term usage across queries, terms used per query, page requests per query, queries per user/ session, query frequency across users). The authors fitted each data set to theoretical models used in Informetric research, in additional to the standard Zipf model to which most frequency distributions of Internet usage phenomena are informally fitted. The generalized inverse Gaussian-Poisson distribution provided the best fits for the models tested. With more accurate models to represent observed Web usage behaviours, researchers will be better able to simulate user behaviours. [ABSTRACT FROM AUTHOR]

Focuses on queries submitted to the Excite search engine for subject content based on the concurrence of terms within multiterm queries. Categorization of the most frequently co-occurring term pairs into developed subject areas; Use of hierarchical cluster analysis and multidimensional scaling; Well-defined high-level clusters of broad subject areas.

Examines how public Web searching has changed from September 16, 1997 to December 20, 1999. Comparative statistics from two studies of Excite queries' data sets; Distribution of sample of queries across subject categories.