An analysis of queries intended to search information for children

Query logs contain valuable information about the behavior, interests, and preferences of the users. The analysis of this information can give insight in their interaction and search behavior. In this paper, we analyze queries and groups of queries intended to find information that is suitable for children by using a large-scale query log. The aim of the analysis it twofold: (i) To identify differences in the query space, content space, user sessions, and user click behavior. (ii) To enhance the query log by including annotations of queries, sessions and actions. The paper presents plans to use this resource for further research on information retrieval for children. We found statistically significant differences between the set of general purpose queries, and the set of children queries. We show that many of these differences are consistent with small-scale research studies in which children were observed while using web search engines.

[1]  Carol Collier Kuhlthau,et al.  Inside the search process: Information seeking from the user's perspective , 1991, J. Am. Soc. Inf. Sci..

[2]  Carol Collier Kuhlthau Inside the Search Process: Information Seeking from the User's Perspective. , 1991 .

[3]  Robert S. Siegler,et al.  Children's thinking, 2nd ed. , 1991 .

[4]  Virginia A. Walter The Information Needs of Children , 1994 .

[5]  Monika Henzinger,et al.  Analysis of a very large web search engine query log , 1999, SIGF.

[6]  Dania Bilal,et al.  Children's use of the Yahooligans! Web search engine: I. Cognitive, physical, and affective behaviors on fact-based search tasks , 2000, J. Am. Soc. Inf. Sci..

[7]  Dania Bilal Children's use of the Yahooligans! Web search engine: I. Cognitive, physical, and affective behaviors on fact‐based search tasks , 2000 .

[8]  Daqing He,et al.  Detecting session boundaries from Web user logs , 2000 .

[9]  Dania Bilal,et al.  Children's use of the Yahooligans! Web search engine: II. Cognitive and physical behaviors on research tasks , 2001, J. Assoc. Inf. Sci. Technol..

[10]  BilalDania Children's use of the Yahooligans! Web search engine , 2001 .

[11]  Amanda Spink,et al.  Searching the Web: the public and their queries , 2001 .

[12]  Dania Bilal,et al.  Children's use of the Yahooligans! Web search engine. III. Cognitive and physical behaviors on fully self-generated search tasks , 2002, J. Assoc. Inf. Sci. Technol..

[13]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[14]  Natalya Keberle,et al.  Capturing Semantics from Search Phrases: Incremental User Personification and Ontology-Driven Query Transformation , 2003, ISTA.

[15]  S. Livingstone,et al.  UK children go online: surveying the experiences of young people and their parents , 2004 .

[16]  Steve Chien,et al.  Semantic similarity between search engine queries using temporal correlation , 2005, WWW '05.

[17]  Massimo Barbaro,et al.  A Face Is Exposed for AOL Searcher No , 2006 .

[18]  Abdur Chowdhury,et al.  A picture of search , 2006, InfoScale '06.

[19]  Ricardo A. Baeza-Yates,et al.  The Intention Behind Web Queries , 2006, SPIRE.

[20]  Ophir Frieder,et al.  Query Phrase Suggestion from Topically Tagged Session Logs , 2006, FQAS.

[21]  Xiaofei He,et al.  Query rewriting using active learning for sponsored search , 2007, SIGIR.

[22]  Rosie Jones,et al.  Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs , 2008, CIKM '08.

[23]  Aristides Gionis,et al.  The query-flow graph: model and applications , 2008, CIKM '08.

[24]  ChengXiang Zhai,et al.  Mining term association patterns from search logs for effective query reformulation , 2008, CIKM '08.

[25]  Mounia Lalmas,et al.  Workshop on aggregated search , 2008, SIGF.

[26]  W. Bruce Croft,et al.  Discovering key concepts in verbose queries , 2008, SIGIR '08.

[27]  Reynaldo Gil-García,et al.  Hierarchical Star Clustering Algorithm for Dynamic Document Collections , 2008, CIARP.

[28]  Amanda Spink,et al.  Determining the informational, navigational, and transactional intent of Web queries , 2008, Inf. Process. Manag..

[29]  Fernando Diaz,et al.  Sources of evidence for vertical selection , 2009, SIGIR.

[30]  Naren Ramakrishnan,et al.  Mining linguistic cues for query expansion: applications to drug interaction search , 2009, CIKM.

[31]  Efthimis N. Efthimiadis,et al.  Analyzing and evaluating query reformulation strategies in web search logs , 2009, CIKM.

[32]  Vitor R. Carvalho,et al.  Reducing long queries using query quality predictors , 2009, SIGIR.

[33]  Elizabeth Foss,et al.  How children search the internet with keyword interfaces , 2009, IDC.

[34]  Daniel Gayo-Avello,et al.  Stratified analysis of AOL query log , 2009, Inf. Sci..

[35]  W. Bruce Croft,et al.  Analysis of long queries in a large scale search log , 2009, WSCD '09.