Big data and social science: a practical guide to methods and tools
Saved in:
| Other Authors: | , , , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
[2017]
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| Series: | Chapman & Hall/CRC statistics in the social and behavioral sciences series
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xxi, 356 Seiten Illustrationen, Diagramme |
| ISBN: | 9781498751407 |
Staff View
MARC
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| 245 | 1 | 0 | |a Big data and social science |b a practical guide to methods and tools |c edited by Ian Foster (University of Chicago, Argonne National Laboratory), Rayid Ghani (University of Chicago), Ron S. Jarmin (U.S. Census Bureau), Frauke Kreuter (University of Maryland, University of Manheim, Institute for Employment Research), Julia Lane (New York University, American Institutes for Research) |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press |c [2017] | |
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Record in the Search Index
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| adam_text | Contents
Preface xiii
Editors xv
Contributors xix
1 Introduction 1
1.1 Why this book?.................................................................. 1
1.2 Defining big data and its value................................................. 3
1.3 Social science, inference, and big data......................................... 4
1.4 Social science, data quality, and big data ..................................... 7
1.5 New tools for new data.......................................................... 9
1.6 The book’s “use case”.......................................................... 10
1.7 The structure of the book...................................................... 13
1.7.1 Part I: Capture and curation........................................ 13
1.7.2 Part II: Modeling and analysis...................................... 15
1.7.3 Part III: Inference and ethics ......................................... 16
1.8 Resources...................................................................... 17
1 Capture and Curation 21
2 Working with Web Data and APIs 23
Cameron Neylon
2.1 Introduction................................................................... 23
2.2 Scraping information from the web.............................................. 24
2.2.1 Obtaining data from the HHMI website.................................... 24
2.2.2 Limits of scraping...................................................... 30
2.3 New data in the research enterprise ........................................... 31
2.4 A functional view.............................................................. 37
2.4.1 Relevant APIs and resources............................................. 38
2.4.2 RESTful APIs, returned data, and Python wrappers....................... 38
2.5 Programming against an API..................................................... 41
Contents
Vi!!
2.6 Using the ORCID API via a wrapper........................................... 42
2.7 Quality, scope, and management.............................................. 44
2.8 Integrating data from multiple sources...................................... 46
2.8.1 The Lagotto API ................................................. 46
2.8.2 Working with a corpus............................................ 52
2.9 Working with the graph of relationships..................................... 58
2.9.1 Citation links between articles.................................. 58
2.9.2 Categories, sources, and connections............................. 60
2.9.3 Data availability and completeness................................... 61
2.9.4 The value of sparse dynamic data..................................... 62
2.10 Bringing it together: Tracking pathways to impact .......................... 65
2.10.1 Network analysis approaches.......................................... 66
2.10.2 Future prospects and new data sources................................ 66
2.11 Summary..................................................................... 67
2.12 Resources................................................................... 69
2.13 Acknowledgements and copyright.............................................. 70
3 Record Linkage 71
Joshua Tokle and Stefan Bender
3.1 Motivation ................................................................. 71
3.2 Introduction to record linkage.............................................. 72
3.3 Preprocessing data for record linkage....................................... 76
3.4 Indexing and blocking....................................................... 78
3.5 Matching.................................................................... 80
3.5.1 Rule-based approaches................................................ 82
3.5.2 Probabilistic record linkage......................................... 83
3.5.3 Machine learning approaches to linking............................... 85
3.5.4 Disambiguating networks.............................................. 88
3.6 Classification.............................................................. 88
3.6.1 Thresholds........................................................... 89
3.6.2 One-to-one links..................................................... 90
3.7 Record linkage and data protection.......................................... 91
3.8 Summary..................................................................... 92
3.9 Resources................................................................... 92
4 Databases 93
Ian Foster and Pascal Heus
4.1 Introduction ............................................................... 93
4.2 DBMS: When and why.......................................................... 94
4.3 Relational DBMSs........................................................... 100
4.3.1 Structured Query Language (SQL)..................................... 102
4.3.2 Manipulating and querying data...................................... 102
4.3.3 Schema design and definition........................................ 105
Contents
IX
4.3.4 Loading data....................................................... 107
4.3.5 Transactions and crash recovery.................................... 108
4.3.6 Database optimizations............................................. 109
4.3.7 Caveats and challenges............................................. 112
4.4 Linking DBMSs and other tools............................................ 113
4.5 NoSQL databases ......................................................... 116
4.5.1 Challenges of scale: The CAP theorem............................... 116
4.5.2 NoSQL and key-value stores ........................................ 117
4.5.3 Other NoSQL databases.............................................. 119
4.6 Spatial databases ....................................................... 120
4.7 Which database to use?................................................... 122
4.7.1 Relational DBMSs................................................... 122
4.7.2 NoSQL DBMSs........................................................ 123
4.8 Summary.................................................................. 123
4.9 Resources................................................................ 124
5 Programming with Big Data 125
Huy Vo and Claudio Silva
5.1 Introduction ............................................................ 125
5.2 The MapReduce programming model.......................................... 127
5.3 Apache Hadoop MapReduce.................................................. 129
5.3.1 The Hadoop Distributed File System............................ 130
5.3.2 Hadoop: Bringing compute to the data............................... 131
5.3.3 Hardware provisioning.............................................. 134
5.3.4 Programming language support....................................... 136
5.3.5 Fault tolerance.................................................... 137
5.3.6 Limitations of Hadoop.............................................. 137
5.4 Apache Spark............................................................. 138
5.5 Summary.................................................................. 141
5.6 Resources................................................................ 143
II Modeling and Analysis 145
6 Machine Learning 1 ¿7
Rayid Ghani and Mate Schierholz
6.1 Introduction ............................................................... 147
6.2 What is machine learning? .................................................. 148
6.3 The machine learning process................................................ 150
6.4 Problem formulation: Mapping a problem to machine learning methods . ... 151
6.5 Methods..................................................................... 153
6.5.1 Unsupervised learning methods........................................ 153
6.5.2 Supervised learning.................................................. 161
X
Contents
6.6 Evaluation.............................................................. 173
6.6.1 Methodology.......................................................... 173
6.6.2 Metrics.............................................................. 176
6.7 Practical tips.......................................................... 180
6.7.1 Features ............................................................ 180
6.7.2 Machine learning pipeline............................................ 181
6.7.3 Multiclass problems.................................................. 181
6.7.4 Skewed or imbalanced classification problems......................... 182
6.8 How can social scientists benefit from machine learning?................ 183
6.9 Advanced topics............................................................ 185
6.10 Summary.................................................................... 185
6.11 Resources.................................................................. 186
7 Text Analysis 187
Evgeny Klochikhin and Jordan Boyd-Graber
7.1 Understanding what people write............................................ 187
7.2 How to analyze text ....................................................... 189
7.2.1 Processing text data................................................. 190
7.2.2 How much is a word worth?............................................ 192
7.3 Approaches and applications................................................ 193
7.3.1 Topic modeling....................................................... 193
7.3.1.1 Inferring topics from raw text ............................ 194
7.3.1.2 Applications of topic models............................... 197
7.3.2 Information retrieval and clustering................................. 198
7.3.3 Other approaches..................................................... 205
7.4 Evaluation................................................................. 208
7.5 Text analysis tools........................................................ 210
7.6 Summary.................................................................... 212
7.7 Resources.................................................................. 213
3 Networks: The Basics 215
Jason Ou’en-Smith
8.1 Introduction .............................................................. 215
8.2 Network data............................................................... 218
8.2.1 Forms of network data................................................ 218
8.2.2 Inducing one-mode networks from two-mode data ....................... 220
8.3 Network measures........................................................... 224
8.3.1 Reachability ........................................................ 224
8.3.2 Whole-network measures............................................... 225
8.4 Comparing collaboration networks........................................... 234
8.5 Summary.................................................................... 238
8.6 Resources.................................................................. 239
Contents
III Inference and Ethics
241
9 Information Visualization
M. Adil Yalçin and Catherine Plaisant
9.1 Introduction....................
9.2 Developing effective visualizations
9.3 A data-by-tasks taxonomy ....
9.3.1 Multivariate data........
9.3.2 Spatial data.............
9.3.3 Temporal data............
9.3.4 Hierarchical data........
9.3.5 Network data.............
9.3.6 Text data................
9.4 Challenges......................
9.4.1 Scalability .............
9.4.2 Evaluation...............
9.4.3 Visual impairment ....
9.4.4 Visual literacy..........
9.5 Summary.........................
9.6 Resources ......................
¿4 J
243
244
249
249
251
252
255
257
259
259
260
261
261
262
262
263
10 Errors and inference 76c
Paul P. Biemer
10.1 Introduction ................................................................. 265
10.2 The total error paradigm...................................................... 266
10.2.1 The traditional model.................................................. 266
10.2.2 Extending the framework to big data................................. 273
10.3 Illustrations of errors in big data........................................... 275
10.4 Errors in big data analytics.................................................. 277
10.4.1 Errors resulting from volume, velocity, and variety, assuming perfect
veracity............................................................... 277
10.4.2 Errors resulting from lack of veracity.............................. 279
10.4.2.1 Variable and correlated error............................. 280
10.4.2.2 Models for categorical data............................... 282
10.4.2.3 Misclassification and rare classes........................... 283
10.4.2.4 Correlation analysis......................................... 284
10.4.2.5 Regression analysis.......................................... 288
10.5 Some methods for mitigating, detecting, and compensating for errors........... 290
10.6 Summary....................................................................... 295
10.7 Resources..................................................................... 296
Contents
XÎ !
11 Privacy and Confidentiality 299
Stefan Bender, Ron Jarmin, Frauke Kreuter, and Julia Lane
11.1 Introduction............................................................... 299
11.2 Why is access important?................................................... 303
11.3 Providing access........................................................... 305
11.4 The new challenges ........................................................ 306
11.5 Legal and ethical framework................................................ 308
11.6 Summary.................................................................... 310
11.7 Resources.................................................................. 311
12 Workbooks 313
Jonathan Scott Morgan, Christina Jones, and Ahmad Emad
12.1 Introduction .............................................................. 313
12.2 Environment................................................................ 314
12.2.1 Running workbooks locally........................................... 314
12.2.2 Central workbook server............................................. 315
12.3 Workbook details........................................................... 315
12.3.1 Social Media and APIs............................................... 315
12.3.2 Database basics..................................................... 316
12.3.3 Data Linkage........................................................ 316
12.3.4 Machine Learning.................................................... 317
12.3.5 Text Analysis....................................................... 317
12.3.6 Networks............................................................ 318
12.3.7 Visualization....................................................... 318
12.4 Resources.................................................................. 319
Bibliography 321
Index
349
BIG DATA AND
SOCIAL SCIENCE
A Practical Guide to Methods and Tools
Big Data and Social Science: A Practical Guide to Methods and Tools shows how
to apply data science to real-world problems in both research and practice. Prominent
leaders in the social sciences, statistics, and computer science as well as the emerging
field of data science provide a unique perspective on how to apply modern social science
research principles and current analytical and computational tools. The text teaches you
how to identify and capture appropriate data, apply data science models and tools to
that data, and recognize and respond to data errors and limitations.
Features
• Takes an accessible, hands-on approach to handling big data in the social sciences
• Presents the key big data tools in a non-intimidating way to both social and data
scientists while not neglecting research questions and purposes
• Illustrates social science and data science principles through real-world applica-
tions
• Links computer science concepts to real social science research
• Promotes good scientific practice
• Provides freely available data and code as well as practical programming exercises
through GitHub
This classroom-tested book fills a major gap in graduate- and professional-level data
and social science education. It can be used to train a new generation of social data
scientists to tackle real-world problems and improve the skills and competencies of ap-
plied social scientists. It empowers you to use the massive and rapidly growing amounts
of available data to interpret economic and social activities in a scientific and rigorous
manner.
|
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| discipline | Soziologie |
| format | Book |
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| series2 | Chapman & Hall/CRC statistics in the social and behavioral sciences series |
| spelling | Big data and social science a practical guide to methods and tools edited by Ian Foster (University of Chicago, Argonne National Laboratory), Rayid Ghani (University of Chicago), Ron S. Jarmin (U.S. Census Bureau), Frauke Kreuter (University of Maryland, University of Manheim, Institute for Employment Research), Julia Lane (New York University, American Institutes for Research) Boca Raton ; London ; New York CRC Press [2017] xxi, 356 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC statistics in the social and behavioral sciences series Includes bibliographical references and index Datenverarbeitung Sozialwissenschaften Social sciences Data processing Social sciences Statistical methods Data mining Big data Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Sozialwissenschaften (DE-588)4055916-6 s Big Data (DE-588)4802620-7 s DE-604 Foster, Ian 1959- (DE-588)122888529 edt Ghani, Rayid edt Jarmin, Ronald S. 1964- (DE-588)124661262 edt Kreuter, Frauke (DE-588)1033254037 edt Lane, Julia 1956- (DE-588)129556807 edt Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029113428&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029113428&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Big data and social science a practical guide to methods and tools Datenverarbeitung Sozialwissenschaften Social sciences Data processing Social sciences Statistical methods Data mining Big data Sozialwissenschaften (DE-588)4055916-6 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4802620-7 (DE-588)4143413-4 |
| title | Big data and social science a practical guide to methods and tools |
| title_auth | Big data and social science a practical guide to methods and tools |
| title_exact_search | Big data and social science a practical guide to methods and tools |
| title_full | Big data and social science a practical guide to methods and tools edited by Ian Foster (University of Chicago, Argonne National Laboratory), Rayid Ghani (University of Chicago), Ron S. Jarmin (U.S. Census Bureau), Frauke Kreuter (University of Maryland, University of Manheim, Institute for Employment Research), Julia Lane (New York University, American Institutes for Research) |
| title_fullStr | Big data and social science a practical guide to methods and tools edited by Ian Foster (University of Chicago, Argonne National Laboratory), Rayid Ghani (University of Chicago), Ron S. Jarmin (U.S. Census Bureau), Frauke Kreuter (University of Maryland, University of Manheim, Institute for Employment Research), Julia Lane (New York University, American Institutes for Research) |
| title_full_unstemmed | Big data and social science a practical guide to methods and tools edited by Ian Foster (University of Chicago, Argonne National Laboratory), Rayid Ghani (University of Chicago), Ron S. Jarmin (U.S. Census Bureau), Frauke Kreuter (University of Maryland, University of Manheim, Institute for Employment Research), Julia Lane (New York University, American Institutes for Research) |
| title_short | Big data and social science |
| title_sort | big data and social science a practical guide to methods and tools |
| title_sub | a practical guide to methods and tools |
| topic | Datenverarbeitung Sozialwissenschaften Social sciences Data processing Social sciences Statistical methods Data mining Big data Sozialwissenschaften (DE-588)4055916-6 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Datenverarbeitung Sozialwissenschaften Social sciences Data processing Social sciences Statistical methods Data mining Big data Big Data Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029113428&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029113428&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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