Temporal analysis of a very large topically categorized Web query log

Abstract

Web queries that constituted the total query traffic for a 6-month period of a general-purpose commercial Web search service. Previously, query logs were studied from a single, cumulative view. In contrast, this study builds on the authors' previous work, which showed changes in popularity and uniqueness of topically categorized queries across the hours in a day. To further their analysis, they examine query traffic on a daily, weekly, and monthly basis by matching it against lists of queries that have been topically precategorized by human editors. These lists represent 13% of the query traffic. They show that query traffic from particular topical categories differs both from the query stream as a whole and from other categories. Additionally, they show that certain categories of queries trend differently over varying periods. The authors key contribution is twofold: They outline a method for studying both the static and topical properties of a very large query log over varying periods, and they identify and examine topical trends that may provide valuable insight for improving both retrieval effectiveness and efficiency.

Seeking electronic information from government resources: A comparative analysis of two communities' web searching of municipal government websites

“…Pu et al (2002) used a similar approach to develop their subject taxonomy for the automatic classification of web query terms into broad subject categories. Beitzel et al (2007) involved the analysis of the web query logs of the America Online (AOL) web search service. However, the authors used a longitudinal analysis "to examine static and topical changes [in querying] over longer periods such as days, weeks, and months" (Beitzel et al, 2007, p. 167).…”

“…A rather significant literature exists currently in information science that studies information seekers' queries submitted through various types of search engines (e.g., Beitzel, Jensen, Chowdhury, Frieder, & Grossman, 2007;Chau, Fang, & Sheng, 2005;Lau & Goh, 2006;Pu, Chuang, & Yang, 2002;Rieh & Xie, 2006;Ross & Wolfram, 2000;Silverstein, Henzinger, Marais, & Moricz, 1999;Spink, Wolfram, Jansen, & Saracevic, 2001;Wang, Berry, & Yang, 2003). However, the published web search literature lacks the analysis of large amounts of unobtrusively collected data such as those found in web logs to determine the types of information sought through government websites in general, and municipal government websites particularly.…”

DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users

“…The intuition is that pages on which users spent more time are more relevant than pages that users leave very quickly. The authors of [3] analyzed an AOL query log in terms of how the query behavior of users changes over time. They found that certain topical categories can exhibit both short-term, i.e.…”

Analysis of the Temporal and Geographical Distribution of a Chinese Learning Website Visitors

“…Beitzel's study focused on investigating the nature of changes in the query stream of a very large search service over. He also proposed a method of temporal log analysis that can be used to study changing topical trends over time in addition to the traditional static analysis [7]. Silverstein et al found that for 85% of the queries in Alta Vista only the first result screen is viewed, and 77% of the sessions contain only 1 query [8].…”