Visual analysis of obesity-related query terms on HealthLink

Purpose – The purpose of this article is to investigate obesity‐related queries from a public health portal (HealthLink) transaction log.Design/methodology/approach – Multidimensional scaling (MDS) was applied to each of five obesity‐related focus keywords and their co‐occurring terms in submitted queries. After the transaction log data were collected and cleaned, and query terms were extracted and parsed, relationships between a focus keyword and its co‐occurring terms were established. Clustering relationships between focus keywords and their co‐occurring terms were identified and analysed in the MDS visual context.Findings – The MDS analysis produced satisfactory outcomes for all five focus keywords. The term “placements”, in the visual configurations revealed strong grouping tendencies of three to five clusters for each focus keyword.Originality/value – The findings of this study provide insights into health consumers' internet‐based information‐seeking behaviour on obesity‐related topics. These findi...

[1]  Oren Etzioni,et al.  Adaptive Web Sites: an AI Challenge , 1997, IJCAI.

[2]  Christopher C. Yang,et al.  Mining related queries from Web search engine query logs using an improved association rule mining model , 2007, J. Assoc. Inf. Sci. Technol..

[3]  Yanchun Zhang,et al.  Clustering of web users using session-based similarity measures , 2001, Proceedings 2001 International Conference on Computer Networks and Mobile Computing.

[4]  Tao Tang,et al.  Visual health subject directory analysis based on users' traversal activities , 2009 .

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

[6]  Tom Peters,et al.  The history and development of transaction log analysis , 1993 .

[7]  Aistis Raudys,et al.  A process of knowledge discovery from web log data: Systematization and critical review , 2007, Journal of Intelligent Information Systems.

[8]  J. Gerberding,et al.  Actual causes of death in the United States, 2000. , 2004, JAMA.

[9]  Amanda Spink,et al.  Web searcher interaction with the Dogpile.com metasearch engine , 2007 .

[10]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[11]  Sally Jo Cunningham,et al.  An Analysis of Usage of a Digital Library , 1998, ECDL.

[12]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[13]  Robert R. Korfhage,et al.  Information Storage and Retrieval , 1963 .

[14]  Jin Zhang,et al.  Visualization for Information Retrieval , 2007, Encyclopedia of Database Systems.

[15]  Michael Grüninger,et al.  Introduction , 2002, CACM.

[16]  Andrew Large,et al.  User search behavior of domain-specific information retrieval systems: An analysis of the query logs from PsycINFO and ABC-Clio's Historical Abstracts-America: History and Life: Research Articles , 2006 .

[17]  Jaideep Srivastava,et al.  Web Mining: Pattern Discovery from World Wide Web Transactions , 1996 .

[18]  Amanda Spink,et al.  Web searching on the Vivisimo search engine , 2006, J. Assoc. Inf. Sci. Technol..

[19]  J. E. Lincoln,et al.  Actual causes of death in the United States. , 1994, JAMA.

[20]  E. Garfield,et al.  The geography of science: disciplinary and national mappings , 1985 .

[21]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[22]  Q. Zeng,et al.  Exploring and Developing Consumer Health Vocabularies , 2005 .

[23]  Mike Thelwall,et al.  An initial exploration of the link relationship between UK university Web sites , 2002, Aslib Proc..

[24]  Yen-Jen Oyang,et al.  Relevant term suggestion in interactive web search based on contextual information in query session logs , 2003, J. Assoc. Inf. Sci. Technol..

[25]  Michael D. Cooper,et al.  Usage patterns of a web-based library catalog , 2001, J. Assoc. Inf. Sci. Technol..

[26]  Dietmar Wolfram,et al.  Citation relationships among international mass communication journals , 1995, J. Inf. Sci..

[27]  Ophir Frieder,et al.  Temporal analysis of a very large topically categorized Web query log , 2007, J. Assoc. Inf. Sci. Technol..

[28]  Ramana Rao,et al.  Silk from a sow's ear: extracting usable structures from the Web , 1996, CHI.

[29]  W. Bruce Croft,et al.  Providing Government Information on the Internet: Experiences with THOMAS , 1995, DL.

[30]  Peiling Wang,et al.  Mining longitudinal web queries: Trends and patterns , 2003, J. Assoc. Inf. Sci. Technol..

[31]  Liwen Vaughan,et al.  Visualizing linguistic and cultural differences using Web co-link data , 2006, J. Assoc. Inf. Sci. Technol..

[32]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[33]  McGinnis Jm,et al.  Actual causes of death in the United States. , 1993 .

[34]  Jin Zhang,et al.  Analysis of query keywords of sports-related queries using visualization and clustering , 2009 .