Spink / Jansen Web Search: Public Searching of the Web

1. Auflage 2006
ISBN: 978-1-4020-2269-2
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 6, 205 Seiten

Reihe: Information Science and Knowledge Management

This book brings together results from the Web search studies we conducted from 1997 through 2004. The aim of our studies has been twofold: to examine how the public at large searches the Web and to highlight trends in public Web searching. The eight-year period from 1997 to 2004 saw the beginnings and maturity of public Web searching. Commercial Web search engines have come and gone, or endured, through the fall of the dot.com companies. We saw the rise and, in some cases, the demise of several high profile, publicly available Web search engines. The study of the Web search is an exciting and important area of interdisciplinary research. Our book provides a valuable insight into the growth and development of human interaction with Web search engines. In this book, our focus is on the human aspect of the interaction between user and Web search engine. We do not investigate the Web search engines themselves or their constantly changing interfaces, algorithms and features. We focus on exploring the cognitive and user aspects of public Web searching in the aggregate. We use a variety of quantitative and qualitative methods within the overall methodology known as transaction log analysis.

Autoren/Hrsg.

Spink, Amanda

Jansen, Bernard J.

Inhaltsverzeichnis

1;Contents;6
2;Preface;8
2.1;PURPOSE AND APPROACH;9
2.2;AUDIENCE;10
2.3;ACKNOWLEDGMENTS;10
3;Foreword;12
4;Section I THE CONTEXT OF WEB SEARCH;15
4.1;Chapter 1 TECHNOLOGICAL, SOCIAL AND ORGANIZATIONAL CONTEXT;17
4.1.1;1. INTRODUCTION;17
4.1.2;2. WEB SEARCH TECHNOLOGY CONTEXT;17
4.1.3;3. WEB SEARCH ENGINES;19
4.1.3.1;3.1 Overview;19
4.1.3.2;3.2 The Web Search Engine Landscape;19
4.1.3.3;3.3 How Web Search Engines Work;21
4.1.3.4;3.4 Methods of Document Ranking;23
4.1.4;4. WEB SIZE;25
4.1.5;5. WEB SEARCHES;25
4.1.6;6. SOCIAL WEB CONTEXT;26
4.1.6.1;6.1 Internet Use at Home;26
4.1.7;7. ORGANIZATION WEB;28
4.1.8;8. CONCLUSION;28
4.1.9;9. REFERENCES;29
4.2;Chapter 2 HUMAN INFORMATION BEHAVIOR AND HUMAN COMPUTER INTERACTION CONTEXT;33
4.2.1;1. INTRODUCTION;33
4.2.2;2. HUMAN INFORMATION BEHAVIOR CONTEXT;33
4.2.3;3. HUMAN COMPUTER INTERACTION CONTEXT;34
4.2.4;4. RESEARCH METHODS;34
4.2.5;5. WEB SEARCH STUDIES;35
4.2.6;6. WEB SEARCH BEHAVIOR STUDIES;35
4.2.6.1;6.1 Web Search Behavior Studies 1995 to 1998;35
4.2.6.2;6.2 Web Search Behavior Studies 1999 to 2001;36
4.2.6.3;6.3 Web Search Behavior Studies 2002 to 2003;38
4.2.7;7. SINGLE WEB SITE SEARCH STUDIES;39
4.2.8;8. WEB INFORMATION FORAGING STUDIES;39
4.2.9;9. CHILDREN’S WEB SEARCH STUDIES;40
4.2.10;10. TRAINING AND LEARNING STUDIES;41
4.2.11;11. WEB SEARCH EVALUATION STUDIES;41
4.2.12;12. CONCLUSION AND FURTHER RESEARCH;42
4.2.13;13. REFERENCES;43
4.3;Chapter 3 RESEARCH DESIGN;49
4.3.1;1. INTRODUCTION;49
4.3.2;2. WEB QUERY TRANSACTION LOG ANALYSIS;49
4.3.3;3. STRENGTHS AND WEAKNESSES OF WEB TRANSACTION LOG ANALYSIS;52
4.3.4;4. WEB SEARCH LOGS;53
4.3.5;5. ALTAVISTA;54
4.3.6;6. EXCITE;55
4.3.7;7. ALLTHEWEB.COM;56
4.3.8;8. WEB QUERY LOG FIELDS;56
4.3.9;9. ANALYSIS LEVELS;57
4.3.10;10. QUANTITATIVE ANALYSIS;58
4.3.11;11. QUALITATIVE METHODS;61
4.3.11.1;11.1 Web Query Classification;61
4.3.11.2;11.2 Topical Analysis;61
4.3.11.3;11.3 Topical Relevance;62
4.3.12;12. STRENGTH AND LIMITATIONS;62
4.3.13;13. CONCLUSION;63
4.3.14;14. REFERENCES;63
5;Section II HOW PEOPLE SEARCH THE WEB;67
5.1;Chapter 4 SEARCH TERMS;69
5.1.1;1. INTRODUCTION;69
5.1.2;2. WEB SEARCH TERM TRENDS;71
5.1.3;3. AGGREGATE DATA BY WEB SEARCH ENGINES;72
5.1.3.1;3.1 AlltheWeb.com;72
5.1.3.2;3.2 AltaVista;73
5.1.3.3;3.3 Excite;74
5.1.4;4. IN-DEPTH ANALYSES BY WEB SEARCH ENGINE;75
5.1.4.1;4.1 AlltheWeb.com;75
5.1.4.2;4.2 AltaVista;79
5.1.4.3;4.3 Excite;83
5.1.5;5. CONCLUSION;88
5.1.6;6. REFERENCES;89
5.2;Chapter 5 SEARCH QUERIES;91
5.2.1;1. INTRODUCTION;91
5.2.2;2. WEB QUERYING;92
5.2.3;3. WEB QUERY STRUCTURE;93
5.2.4;4. WEB SEARCH ENGINE QUERY TRENDS;93
5.2.5;5. AGGREGATE DATA BY SEARCH ENGINE;95
5.2.5.1;5.1 AlltheWeb.com;95
5.2.5.2;5.2 AltaVista;96
5.2.5.3;5.3 Excite;97
5.2.6;6. ALLTHEWEB.COM IN-DEPTH ANALYSIS;97
5.2.6.1;6.1 Query Length;97
5.2.6.2;6.2 Use of Advanced Web Search Features;98
5.2.6.3;6.3 Repeat Web Queries;99
5.2.6.4;6.4 Language Preference;100
5.2.6.5;6.5 Web Documents Viewed Per Query;102
5.2.7;7. ALTAVISTA IN-DEPTH ANALYSIS;102
5.2.7.1;7.1 Web Query Length;102
5.2.7.2;7.2 Use of Advanced Web Search Features;103
5.2.7.3;7.3 Repeat Web Queries;104
5.2.8;8. EXCITE IN-DEPTH ANALYSIS;107
5.2.8.1;8.1 Web Query Length;107
5.2.8.2;8.2 Use of Advanced Search Features;107
5.2.8.3;8.3 Repeat Web Queries;108
5.2.9;9. NATURAL LANGUAGE WEB QUERIES;110
5.2.10;10. CONCLUSION;111
5.2.11;11. REFERENCES;112
5.3;Chapter 6 SEARCH SESSIONS;115
5.3.1;1. INTRODUCTION;115
5.3.2;2. WEB SEARCH SESSIONS;116
5.3.3;3. WEB SEARCH ENGINE SESSIONS TRENDS;117
5.3.4;4. AGGREGATE DATA BY WEB SEARCH ENGINE;118
5.3.4.1;4.1 AlltheWeb.com;118
5.3.4.2;4.2 AltaVista;119
5.3.4.3;4.3 Excite;120
5.3.5;5. ALLTHEWEB.COM IN-DEPTH ANALYSIS;121
5.3.5.1;5.1 Web Session Length;121
5.3.5.2;5.2 Web Session Duration;122
5.3.5.3;5.3 Results Pages Viewed;123
5.3.5.4;5.4 Click Through Analysis;124
5.3.5.5;5.5 Topical Relevance of Documents Viewed;125
5.3.6;6. ALTAVISTA IN-DEPTH ANALYSIS;126
5.3.6.1;6.1 Session Length;126
5.3.6.2;6.2 Web Session Duration;127
5.3.6.3;6.3 Results Pages Viewed;129
5.3.7;7. EXCITE IN-DEPTH ANALYSIS;130
5.3.7.1;7.1 Session Length;130
5.3.7.2;7.2 Results Pages Viewed;130
5.3.8;8. AGENT SESSIONS;131
5.3.9;9. SUCCESSIVE SEARCH SESSIONS;133
5.3.10;10. MULTITASKING SEARCH SESSIONS;134
5.3.11;11. CONCLUSION;135
5.3.12;12. REFERENCES;136
6;Section III SUBJECTS OF WEB SEARCH;139
6.1;Chapter 7 E-COMMERCE WEB SEARCHING;141
6.1.1;1. INTRODUCTION;141
6.1.2;2. WEB E- COMMERCE;142
6.1.3;3. E-COMMERCE WEB SEARCH;143
6.1.4;4. TRENDS ANALYSIS;144
6.1.5;5. E-COMMERCE WEB QUERY TRENDS;145
6.1.6;6. EXCITE 2001 E-COMMERCE SESSIONS;146
6.1.6.1;6.1 E-Commerce Query Structure;146
6.1.6.2;6.2 Excite E-Commerce Query Subjects;147
6.1.7;7. E-COMMERCE WEB SEARCH TRENDS;147
6.1.8;8. CONCLUSION;149
6.1.9;9. REFERENCES;149
6.2;Chapter 8 MEDICAL AND HEALTH WEB SEARCHING;151
6.2.1;1. INTRODUCTION;151
6.2.2;2. RELATED STUDIES;152
6.2.3;3. MEDICAL WEB SEARCHING;153
6.2.4;4. MEDICAL/HEALTH QUERIES;154
6.2.5;5. MEDICAL ADVICE- SEEKING;155
6.2.5.1;5.1 General Medical/Health;156
6.2.5.2;5.2 Human Relationships;156
6.2.5.3;5.3 Weight;156
6.2.5.4;5.4 Reproductive Health;156
6.2.5.5;5.5 Pregnancy/Baby;156
6.2.6;6. MEDICAL AND HEALTH ADVICE-SEEKING;156
6.2.6.1;6.1 Personified and Opinion Queries;157
6.2.7;7. DISCUSSION;158
6.2.8;8. REFERENCES;159
6.3;Chapter 9 SEXUALLY-RELATED WEB SEARCHING;163
6.3.1;1. INTRODUCTION;163
6.3.2;2. HUMAN INTERNET SEXUALITY;163
6.3.3;3. SEXUALITY AND WEB SEARCHING;164
6.3.4;4. SEXUALLY-RELATED WEB SEARCHING;165
6.3.5;5. TRENDS IN SEXUAL WEB SEARCHING;167
6.3.6;6. ALLTHEWEB.COM QUERIES;168
6.3.7;7. ALTAVISTA QUERIES;169
6.3.8;8. DISCUSSION;171
6.3.9;9. CONCLUSION;172
6.3.10;10. REFERENCES;173
6.4;Chapter 10 MULTIMEDIA SEARCHING;175
6.4.1;1. INTRODUCTION;175
6.4.2;2. IMAGE RETRIEVAL;176
6.4.3;3. MULTIMEDIA SEARCHING;178
6.4.4;4. MULTIMEDIA WEB SEARCHING TRENDS;178
6.4.5;5. DATA COLLECTION;182
6.4.6;6. MULTIMEDIA WEB SEARCH USING DISTINCT CONTENT COLLECTIONS;183
6.4.7;7. MULTIMEDIA SESSIONS;184
6.4.8;8. MULTIMEDIA QUERIES;185
6.4.9;9. MULTIMEDIA WEB TERMS;186
6.4.10;10. DISCUSSION;187
6.4.11;11. CONCLUSION;189
6.4.12;12. REFERENCES;190
7;Section IV CONCLUSION;193
7.1;Chapter 11 KEY FINDINGS, TRENDS, FURTHER RESEARCH AND CONCLUSIONS;195
7.1.1;1. KEY FINDINGS;195
7.1.2;2. SOCIAL AND ORGANIZATIONAL RESEARCH;195
7.1.3;3. COGNITIVE RESEARCH;196
7.1.4;4. RESEARCH METHODS;196
7.1.5;5. COMMON SEARCH CHARACTERISTICS;197
7.1.6;6. SEARCH TOPICS;197
7.1.7;7. QUERY LENGTH;198
7.1.8;8. BOOLEAN OPERATOR USAGE;198
7.1.9;9. SEARCH SESSION LENGTH;199
7.1.10;10. PAGE VIEWING;199
7.1.11;11. GEOGRAPHIC DIFFERENCES;200
7.1.12;12. E-COMMERCE QUERIES;200
7.1.13;13. MEDICAL QUERIES;201
7.1.14;14. SEXUAL QUERIES;201
7.1.15;15. MULTIMEDIA QUERIES;201
7.1.16;16. TRAINING STUDIES AND SEARCH ENGINE EVALUATIONS;202
7.1.17;17. WEB SEARCH TRENDS;203
7.1.18;18. CONCLUSIONS;203
7.1.19;19. REFERENCES;204
8;SUBJECT INDEX;205
9;AUTHOR INDEX;207

Leseproben

Chapter 4 (p. 55-56)

SEARCH TERMS


1. INTRODUCTION

This chapter reports results from an analysis of the search terms submitted to Web search engines – AlltheWeb.com, AltaVista and Excite. Terms are the basic building blocks through which a Web searcher expresses their information problem when searching on a Web search engine. Single or multiple term and operators form a Web query. What are the subjects of Web users’ search terms? Where do the search terms come from? Why does a user select one term instead of another? What influences a searcher’s decisions?

Major findings suggest: (1) the topic interests of Web search engine users has shifted to commercial and informational from the sexual and technology domains, (2) the information problems of Web search engine users are becoming increasingly more diverse, (3) there is a notable increase in non- English terms, numbers, and acronyms used as Web search terms, (4) a set of approximately 20% of search terms are used with great regularity while approximately 10% of the terms are used only once, and (5) major news events and holidays influence search term usage.

Many researchers view Web search as a communication process in which there is a dialog or discourse occurring between the searcher and the Web search engine (Jansen, 2003; Spink, 1997). A dialog is a communication exchange about a certain topic between a user and a Web search engine that includes thinking on the part of the user. Iivonen and Sonnenwald (1998) note that when selecting search terms, searchers appear to navigate a variety of dialogs. Searchers evaluate and synthesize information among these dialogs in order to select search terms.

Hsieh-Yee (1993) reports that the level of a user’s search experience and domain knowledge affects the searchers' selection of search terms. Along with domain knowledge and searching experience, Spink and Saracevic (1997) identified three other sources of search terms pertinent to Web searching, namely (1) the users' level of domain knowledge of their search topic, (2) the Web systems output, and (3) a thesaurus or related terms. They noted that search terms from the user’s domain and the system’s output were the terms that helped the most in retrieving relevant documents.

Researchers have also investigated reformulation (Dennis, Bruza and McArthur, 2002) and search term weighting in order to improve performance. The underlying assumption is that not all terms in a query are of equal importance. The most well known case being that of stop words (Fox, 1990), which are query terms that occur so frequently that they are deemed of little content value (e.g. and, or, the). Some Web search engines automatically remove stop words from queries unless the user specifically tells the search engine (via query operators such as PHRASE or MUST APPEAR) to keep them in the query. Members of some communities refer to stop words as filter words (WebMasterWorld.com, 2004), in which case stop words refer to terms in Web documents that cause a Web search engine spider to stop indexing.

The idea behind term weighting is that the terms with the most importance should have more effect on the retrieval process. Budzik, Hammond, and Birnbaum (2001) use a version of term weighting in an application to automatically formulate queries. Some Web search engines have attempted to implement term weighting automatically using clickthrough data from query transaction logs (Schaale, Wulf-Mathies and Lieberam-Schmidt, 2003).