Web Search: Public Searching of the Web (eBook)

Contents 6
Preface 8
PURPOSE AND APPROACH 9
AUDIENCE 10
ACKNOWLEDGMENTS 10
Foreword 12
Section I THE CONTEXT OF WEB SEARCH 15
Chapter 1 TECHNOLOGICAL, SOCIAL AND ORGANIZATIONAL CONTEXT 17
1. INTRODUCTION 17
2. WEB SEARCH TECHNOLOGY CONTEXT 17
3. WEB SEARCH ENGINES 19
3.1 Overview 19
3.2 The Web Search Engine Landscape 19
3.3 How Web Search Engines Work 21
3.4 Methods of Document Ranking 23
4. WEB SIZE 25
5. WEB SEARCHES 25
6. SOCIAL WEB CONTEXT 26
6.1 Internet Use at Home 26
7. ORGANIZATION WEB 28
8. CONCLUSION 28
9. REFERENCES 29
Chapter 2 HUMAN INFORMATION BEHAVIOR AND HUMAN COMPUTER INTERACTION CONTEXT 33
1. INTRODUCTION 33
2. HUMAN INFORMATION BEHAVIOR CONTEXT 33
3. HUMAN COMPUTER INTERACTION CONTEXT 34
4. RESEARCH METHODS 34
5. WEB SEARCH STUDIES 35
6. WEB SEARCH BEHAVIOR STUDIES 35
6.1 Web Search Behavior Studies 1995 to 1998 35
6.2 Web Search Behavior Studies 1999 to 2001 36
6.3 Web Search Behavior Studies 2002 to 2003 38
7. SINGLE WEB SITE SEARCH STUDIES 39
8. WEB INFORMATION FORAGING STUDIES 39
9. CHILDREN’S WEB SEARCH STUDIES 40
10. TRAINING AND LEARNING STUDIES 41
11. WEB SEARCH EVALUATION STUDIES 41
12. CONCLUSION AND FURTHER RESEARCH 42
13. REFERENCES 43
Chapter 3 RESEARCH DESIGN 49
1. INTRODUCTION 49
2. WEB QUERY TRANSACTION LOG ANALYSIS 49
3. STRENGTHS AND WEAKNESSES OF WEB TRANSACTION LOG ANALYSIS 52
4. WEB SEARCH LOGS 53
5. ALTAVISTA 54
6. EXCITE 55
7. ALLTHEWEB.COM 56
8. WEB QUERY LOG FIELDS 56
9. ANALYSIS LEVELS 57
10. QUANTITATIVE ANALYSIS 58
11. QUALITATIVE METHODS 61
11.1 Web Query Classification 61
11.2 Topical Analysis 61
11.3 Topical Relevance 62
12. STRENGTH AND LIMITATIONS 62
13. CONCLUSION 63
14. REFERENCES 63
Section II HOW PEOPLE SEARCH THE WEB 67
Chapter 4 SEARCH TERMS 69
1. INTRODUCTION 69
2. WEB SEARCH TERM TRENDS 71
3. AGGREGATE DATA BY WEB SEARCH ENGINES 72
3.1 AlltheWeb.com 72
3.2 AltaVista 73
3.3 Excite 74
4. IN-DEPTH ANALYSES BY WEB SEARCH ENGINE 75
4.1 AlltheWeb.com 75
4.2 AltaVista 79
4.3 Excite 83
5. CONCLUSION 88
6. REFERENCES 89
Chapter 5 SEARCH QUERIES 91
1. INTRODUCTION 91
2. WEB QUERYING 92
3. WEB QUERY STRUCTURE 93
4. WEB SEARCH ENGINE QUERY TRENDS 93
5. AGGREGATE DATA BY SEARCH ENGINE 95
5.1 AlltheWeb.com 95
5.2 AltaVista 96
5.3 Excite 97
6. ALLTHEWEB.COM IN-DEPTH ANALYSIS 97
6.1 Query Length 97
6.2 Use of Advanced Web Search Features 98
6.3 Repeat Web Queries 99
6.4 Language Preference 100
6.5 Web Documents Viewed Per Query 102
7. ALTAVISTA IN-DEPTH ANALYSIS 102
7.1 Web Query Length 102
7.2 Use of Advanced Web Search Features 103
7.3 Repeat Web Queries 104
8. EXCITE IN-DEPTH ANALYSIS 107
8.1 Web Query Length 107
8.2 Use of Advanced Search Features 107
8.3 Repeat Web Queries 108
9. NATURAL LANGUAGE WEB QUERIES 110
10. CONCLUSION 111
11. REFERENCES 112
Chapter 6 SEARCH SESSIONS 115
1. INTRODUCTION 115
2. WEB SEARCH SESSIONS 116
3. WEB SEARCH ENGINE SESSIONS TRENDS 117
4. AGGREGATE DATA BY WEB SEARCH ENGINE 118
4.1 AlltheWeb.com 118
4.2 AltaVista 119
4.3 Excite 120
5. ALLTHEWEB.COM IN-DEPTH ANALYSIS 121
5.1 Web Session Length 121
5.2 Web Session Duration 122
5.3 Results Pages Viewed 123
5.4 Click Through Analysis 124
5.5 Topical Relevance of Documents Viewed 125
6. ALTAVISTA IN-DEPTH ANALYSIS 126
6.1 Session Length 126
6.2 Web Session Duration 127
6.3 Results Pages Viewed 129
7. EXCITE IN-DEPTH ANALYSIS 130
7.1 Session Length 130
7.2 Results Pages Viewed 130
8. AGENT SESSIONS 131
9. SUCCESSIVE SEARCH SESSIONS 133
10. MULTITASKING SEARCH SESSIONS 134
11. CONCLUSION 135
12. REFERENCES 136
Section III SUBJECTS OF WEB SEARCH 139
Chapter 7 E-COMMERCE WEB SEARCHING 141
1. INTRODUCTION 141
2. WEB E- COMMERCE 142
3. E-COMMERCE WEB SEARCH 143
4. TRENDS ANALYSIS 144
5. E-COMMERCE WEB QUERY TRENDS 145
6. EXCITE 2001 E-COMMERCE SESSIONS 146
6.1 E-Commerce Query Structure 146
6.2 Excite E-Commerce Query Subjects 147
7. E-COMMERCE WEB SEARCH TRENDS 147
8. CONCLUSION 149
9. REFERENCES 149
Chapter 8 MEDICAL AND HEALTH WEB SEARCHING 151
1. INTRODUCTION 151
2. RELATED STUDIES 152
3. MEDICAL WEB SEARCHING 153
4. MEDICAL/HEALTH QUERIES 154
5. MEDICAL ADVICE- SEEKING 155
5.1 General Medical/Health 156
5.2 Human Relationships 156
5.3 Weight 156
5.4 Reproductive Health 156
5.5 Pregnancy/Baby 156
6. MEDICAL AND HEALTH ADVICE-SEEKING 156
6.1 Personified and Opinion Queries 157
7. DISCUSSION 158
8. REFERENCES 159
Chapter 9 SEXUALLY-RELATED WEB SEARCHING 163
1. INTRODUCTION 163
2. HUMAN INTERNET SEXUALITY 163
3. SEXUALITY AND WEB SEARCHING 164
4. SEXUALLY-RELATED WEB SEARCHING 165
5. TRENDS IN SEXUAL WEB SEARCHING 167
6. ALLTHEWEB.COM QUERIES 168
7. ALTAVISTA QUERIES 169
8. DISCUSSION 171
9. CONCLUSION 172
10. REFERENCES 173
Chapter 10 MULTIMEDIA SEARCHING 175
1. INTRODUCTION 175
2. IMAGE RETRIEVAL 176
3. MULTIMEDIA SEARCHING 178
4. MULTIMEDIA WEB SEARCHING TRENDS 178
5. DATA COLLECTION 182
6. MULTIMEDIA WEB SEARCH USING DISTINCT CONTENT COLLECTIONS 183
7. MULTIMEDIA SESSIONS 184
8. MULTIMEDIA QUERIES 185
9. MULTIMEDIA WEB TERMS 186
10. DISCUSSION 187
11. CONCLUSION 189
12. REFERENCES 190
Section IV CONCLUSION 193
Chapter 11 KEY FINDINGS, TRENDS, FURTHER RESEARCH AND CONCLUSIONS 195
1. KEY FINDINGS 195
2. SOCIAL AND ORGANIZATIONAL RESEARCH 195
3. COGNITIVE RESEARCH 196
4. RESEARCH METHODS 196
5. COMMON SEARCH CHARACTERISTICS 197
6. SEARCH TOPICS 197
7. QUERY LENGTH 198
8. BOOLEAN OPERATOR USAGE 198
9. SEARCH SESSION LENGTH 199
10. PAGE VIEWING 199
11. GEOGRAPHIC DIFFERENCES 200
12. E-COMMERCE QUERIES 200
13. MEDICAL QUERIES 201
14. SEXUAL QUERIES 201
15. MULTIMEDIA QUERIES 201
16. TRAINING STUDIES AND SEARCH ENGINE EVALUATIONS 202
17. WEB SEARCH TRENDS 203
18. CONCLUSIONS 203
19. REFERENCES 204
SUBJECT INDEX 205
AUTHOR INDEX 207

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).