Computational analysis of communication: a practical introduction to the analysis of texts, networks, and images with code examples in Python and R
"The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley Blackwell
2022
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Zusammenfassung: | "The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before"-- |
| Beschreibung: | xiii, 314 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119680239 9781119680277 1119680239 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047643774 | ||
| 003 | DE-604 | ||
| 005 | 20221012 | ||
| 007 | t | ||
| 008 | 211215s2022 a||| b||| 00||| eng d | ||
| 020 | |a 9781119680239 |c Paperback |9 978-1-119-68023-9 | ||
| 020 | |a 9781119680277 |c Hardcover |9 978-1-119-68027-7 | ||
| 020 | |a 1119680239 |9 1119680239 | ||
| 035 | |a (OCoLC)1302320071 | ||
| 035 | |a (DE-599)BVBBV047643774 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-188 |a DE-739 |a DE-384 |a DE-20 |a DE-19 | ||
| 084 | |a AP 15942 |0 (DE-625)6955: |2 rvk | ||
| 084 | |a MR 5900 |0 (DE-625)123525: |2 rvk | ||
| 100 | 1 | |a Atteveldt, Wouter van |e Verfasser |0 (DE-588)115041099X |4 aut | |
| 245 | 1 | 0 | |a Computational analysis of communication |b a practical introduction to the analysis of texts, networks, and images with code examples in Python and R |c Wouter van Atteveldt, Vrije Universiteit Amsterdam, Damian Trilling, University of Amersterdam, Carlos Arcíla Calderón, University of Salamanca |
| 264 | 1 | |a Hoboken, NJ |b Wiley Blackwell |c 2022 | |
| 300 | |a xiii, 314 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | 3 | |a "The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before"-- | |
| 650 | 0 | 7 | |a Netzwerkanalyse |g Soziologie |0 (DE-588)4205975-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computerunterstützte Kommunikation |0 (DE-588)4535905-2 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Social sciences / Network analysis | |
| 653 | 0 | |a Communication / Network analysis | |
| 653 | 0 | |a Computational linguistics / Network analysis | |
| 653 | 0 | |a Communication / Data processing | |
| 653 | 0 | |a Communication / Data processing | |
| 653 | 0 | |a Communication / Network analysis | |
| 653 | 0 | |a Computational linguistics / Network analysis | |
| 653 | 0 | |a Social sciences / Network analysis | |
| 689 | 0 | 0 | |a Computerunterstützte Kommunikation |0 (DE-588)4535905-2 |D s |
| 689 | 0 | 1 | |a Netzwerkanalyse |g Soziologie |0 (DE-588)4205975-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Trilling, Damian |d 1983- |e Verfasser |0 (DE-588)1253430918 |4 aut | |
| 700 | 1 | |a Arcíla Calderón, Carlos |e Verfasser |0 (DE-588)106066335X |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 978-1-119-68027-7 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-1-119-68028-4 |
| 856 | 4 | 2 | |m Digitalisierung UB Augsburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 856 | 4 | 2 | |m Digitalisierung UB Augsburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033027953 | ||
Datensatz im Suchindex
| _version_ | 1804183103130828800 |
|---|---|
| adam_text | Contents Preface xi Acknowledgments xiii í 1.1 1.2 1.3 1.4 1.4.1 1.4.2 1.5 Introduction 1 The Role of Computational Analysis in the Social Sciences Why Python and/or R? 3 How to Use This Book 4 Installing R and Python 5 Installing R and RStudio 7 Installing Python and Jupyter Notebook 9 Installing Third-Party Packages 12 2 Getting Started: Fun with Data and Visualizations 2.1 2.2 2.3 2.4 Fun With Tweets 14 Fun With Textual Data 15 Fun With Visualizing Geographic Information 17 Fun With Networks 19 3 3.1 3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 3.2 3.2.1 3.2.2 3.3 Programming Concepts for Data Analysis 23 About Objects and Data Types 24 Storing Single Values: Integers, Floating-Point Numbers, Booleans 25 Storing Text 26 Combining Multiple Values: Lists, Vectors, And Friends 28 Dictionaries 32 From One to More Dimensions: Matrices and n-Dimensional Arrays 33 Making Life Easier: Data Frames 34 Simple Control Structures: Loops and Conditions 35 Loops 36 Conditional Statements 37 Functions and Methods 39 4 43 Re-using Code: How Not to Re-Invent the Wheel 43 Understanding Errors and Getting Help 46 Error Messages 46 4.1 4.2 4.2.1 How to Write Code 1 13
viii Contents 4.2.2 4.3 Debugging Strategies 48 Best Practice: Beautiful Code, GitHub, and Notebooks 5 From File to Data Frame and Back 55 Why and When Do We Use Data Frames? Reading and Saving Data 57 The Role of Files 57 Encodings and Dialects 59 File Handling Beyond Data Frames 61 Data from Online Sources 62 5.1 5.2 5.2.1 5.2.2 5.2.3 5.3 56 6.1 6.2 6.3 6.3.1 6.3.2 6.4 6.4.1 6.4.2 6.4.3 6.5 6.6 Data Wrangling 65 Filtering, Selecting, and Renaming 66 Calculating Values 67 Grouping and Aggregating 69 Combining Multiple Operations 70 Adding Summary Values 71 Merging Data 72 Equal Units of Analysis 72 Inner and Outer Joins 75 Nested Data 76 Reshaping Data: Wide To Long And Long To Wide Restructuring Messy Data 79 7 Exploratory Data Analysis 6 7.1 7.2 7,2.1 7.2.2 7.2.3 7.2.4 7.3 7.3.1 7.3.2 7.3.3 8 8.1 8.2 8.3 8.3.1 8.3.2 8.3.3 8,3.4 8.3.5 8.4 8.4.1 49 78 83 Simple Exploratory Data Analysis 84 Visualizing Data 87 Plotting Frequencies and Distributions 88 Plotting Relationships 92 Plotting Geospatial Data 98 Other Possibilities 99 Clustering and Dimensionality Reduction 100 к-means Clustering 101 Hierarchical Clustering 102 Principal Component Analysis and Singular Value Decomposition 113 Statistical Modeling and Prediction 115 Concepts and Principles 117 Classical Machine Learning: From Naive Bayes to Neural Networks Naïve Bayes 122 Logistic Regression 124 Support Vector Machines 125 Decision Trees and Random Forests 127 Neural Networks 129 Deep Learning 130 Convolutional Neural Networks 131 106 Statistical Modeling and Supervised Machine Learning 122
Contents 8.5 8.5.1 8.5.2 8.5.3 Validation and Best Practices 133 Finding a Balance Between Precision and Recall Train, Validate, Test 137 Cross-validation and Grid Search 138 9 Processing Text 141 Text as a String of Characters 142 Methods for Dealing With Text 144 Regular Expressions 145 Regular Expression Syntax 146 Example Patterns 147 Using Regular Expressions in Python and R 150 Splitting and Joining Strings, and Extracting Multiple Matches 9.1 9.1.1 9.2 9.2.1 9.2.2 9.3 9.3.1 10 10.1 10.1.1 10.1.2 10.1.3 10.1.4 10.2 10.2.1 10.2.2 10.2.3 10.2.4 10.3 10.3.1 10.2.3 10.3.3 10.3.4 10.4 11 11.1 11.2 11.3 11.4 11.4.1 11.4.2 11.4.3 11.4.4 11.5 11.5.1 11.5.2 11.5.3 11.5.4 11.5.5 133 151 155 The Bag of Words and the Term-Document Matrix 156 Tokenization 157 The DTM as a Sparse Matrix 159 The DTM as a “Bag of Words” 162 The (Unavoidable) Word Cloud 163 Weighting and Selecting Documents and Terms 164 Removing stop words 165 Removing Punctuation and Noise 167 Trimming a DTM 170 Weighting a DTM 171 Advanced Representation of Text 172 n-grams 173 Collocations 174 Word Embeddings 176 Linguistic Preprocessing 177 Which Preprocessing to Use? 182 Text as Data 184 Deciding on the Right Method 185 Obtaining a Review Dataset 187 Dictionary Approaches to Text Analysis 189 Supervised Text Analysis: Automatic Classification and Sentiment Analysis 191 Putting Together a Workflow 191 Finding the Best Classifier 194 Using the Model 198 Deep Learning 199 Unsupervised Text Analysis: Topic Modeling 203 Latent Dirichlet Allocation (LDA) 203 Fitting an LDA Model 206 Analyzing Topic Model Results 207
Validating and Inspecting Topic Models 208 Beyond LDA 209 Automatic Analysis of Text ix
X ļ Contents 12.1 12.2 12.2.1 12.2.2 12.2.3 12.3 12.3.1 12.3.2 12.4 Scraping Online Data 212 Using Web APIs: From Open Resources to Twitter 213 Retrieving and Parsing Web Pages 219 Retrieving and Parsing an HTML Page 219 Crawling Websites 223 Dynamic Web Pages 225 Authentication, Cookies, and Sessions 228 Authentication and APIs 228 Authentication and Webpages 229 Ethical, Lega!, and Practical Considerations 230 13 Network Data 12 13.1 13.2 13.2.1 13.2.2 13.2.3 233 Representing and Visualizing Networks 234 Social Network Analysis 241 Paths and Reachability 242 Centrality Measures 246 Clustering and Community Detection 248 258 14.1 Beyond Text Analysis: Images, Audio and Video 259 14.2 Using Existing Libraries and APIs 261 14.3 Storing, Representing, and Converting Images 263 14.4 Image Classification 270 14.4.1 Basic Classification with Shallow Algorithms 272 14.4.2 Deep Learning for Image Analysis 273 14.4.3 Re-using an Open Source CNN 279 14 Multimedia Data 15 Scaling Up and Distributing 283 15.1 Storing Data in SQL and noSQL Databases 283 15.1.1 When to Use a Database 283 15.1.2 Choosing the Right Database 285 15.1.3 A Brief Example Using SQLite 286 15.2 Using Cloud Computing 286 15.3 Publishing Your Source 290 15.4 Distributing Your Software as Container 291 16 16.1 16.2 16.3 Where to Go Next 293 How Far Have We Come? 293 Where To Go Next? 294 Open, Transparent, and Ethical Computational Science 295 Bibliography 297 Index 303
A practicai introduction to the analysis of texts, networks, and code exampies in Python and R In disciplines such as political science, sociology, psychology, communication science, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computational linguistics, this accessible textbook teaches readers how to use state-of-the-art computational methods to perform data-driven analyses of social science issues. A team of authors with expertise in both the social sciences and computer science explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource՛ Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide a unified approach to the subject Includes sample code in Python and R and
links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Computational Analysis of Communication is an invaluable textbook and reference for students taking com putational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
|
| adam_txt |
Contents Preface xi Acknowledgments xiii í 1.1 1.2 1.3 1.4 1.4.1 1.4.2 1.5 Introduction 1 The Role of Computational Analysis in the Social Sciences Why Python and/or R? 3 How to Use This Book 4 Installing R and Python 5 Installing R and RStudio 7 Installing Python and Jupyter Notebook 9 Installing Third-Party Packages 12 2 Getting Started: Fun with Data and Visualizations 2.1 2.2 2.3 2.4 Fun With Tweets 14 Fun With Textual Data 15 Fun With Visualizing Geographic Information 17 Fun With Networks 19 3 3.1 3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 3.2 3.2.1 3.2.2 3.3 Programming Concepts for Data Analysis 23 About Objects and Data Types 24 Storing Single Values: Integers, Floating-Point Numbers, Booleans 25 Storing Text 26 Combining Multiple Values: Lists, Vectors, And Friends 28 Dictionaries 32 From One to More Dimensions: Matrices and n-Dimensional Arrays 33 Making Life Easier: Data Frames 34 Simple Control Structures: Loops and Conditions 35 Loops 36 Conditional Statements 37 Functions and Methods 39 4 43 Re-using Code: How Not to Re-Invent the Wheel 43 Understanding Errors and Getting Help 46 Error Messages 46 4.1 4.2 4.2.1 How to Write Code 1 13
viii Contents 4.2.2 4.3 Debugging Strategies 48 Best Practice: Beautiful Code, GitHub, and Notebooks 5 From File to Data Frame and Back 55 Why and When Do We Use Data Frames? Reading and Saving Data 57 The Role of Files 57 Encodings and Dialects 59 File Handling Beyond Data Frames 61 Data from Online Sources 62 5.1 5.2 5.2.1 5.2.2 5.2.3 5.3 56 6.1 6.2 6.3 6.3.1 6.3.2 6.4 6.4.1 6.4.2 6.4.3 6.5 6.6 Data Wrangling 65 Filtering, Selecting, and Renaming 66 Calculating Values 67 Grouping and Aggregating 69 Combining Multiple Operations 70 Adding Summary Values 71 Merging Data 72 Equal Units of Analysis 72 Inner and Outer Joins 75 Nested Data 76 Reshaping Data: Wide To Long And Long To Wide Restructuring Messy Data 79 7 Exploratory Data Analysis 6 7.1 7.2 7,2.1 7.2.2 7.2.3 7.2.4 7.3 7.3.1 7.3.2 7.3.3 8 8.1 8.2 8.3 8.3.1 8.3.2 8.3.3 8,3.4 8.3.5 8.4 8.4.1 49 78 83 Simple Exploratory Data Analysis 84 Visualizing Data 87 Plotting Frequencies and Distributions 88 Plotting Relationships 92 Plotting Geospatial Data 98 Other Possibilities 99 Clustering and Dimensionality Reduction 100 к-means Clustering 101 Hierarchical Clustering 102 Principal Component Analysis and Singular Value Decomposition 113 Statistical Modeling and Prediction 115 Concepts and Principles 117 Classical Machine Learning: From Naive Bayes to Neural Networks Naïve Bayes 122 Logistic Regression 124 Support Vector Machines 125 Decision Trees and Random Forests 127 Neural Networks 129 Deep Learning 130 Convolutional Neural Networks 131 106 Statistical Modeling and Supervised Machine Learning 122
Contents 8.5 8.5.1 8.5.2 8.5.3 Validation and Best Practices 133 Finding a Balance Between Precision and Recall Train, Validate, Test 137 Cross-validation and Grid Search 138 9 Processing Text 141 Text as a String of Characters 142 Methods for Dealing With Text 144 Regular Expressions 145 Regular Expression Syntax 146 Example Patterns 147 Using Regular Expressions in Python and R 150 Splitting and Joining Strings, and Extracting Multiple Matches 9.1 9.1.1 9.2 9.2.1 9.2.2 9.3 9.3.1 10 10.1 10.1.1 10.1.2 10.1.3 10.1.4 10.2 10.2.1 10.2.2 10.2.3 10.2.4 10.3 10.3.1 10.2.3 10.3.3 10.3.4 10.4 11 11.1 11.2 11.3 11.4 11.4.1 11.4.2 11.4.3 11.4.4 11.5 11.5.1 11.5.2 11.5.3 11.5.4 11.5.5 133 151 155 The Bag of Words and the Term-Document Matrix 156 Tokenization 157 The DTM as a Sparse Matrix 159 The DTM as a “Bag of Words” 162 The (Unavoidable) Word Cloud 163 Weighting and Selecting Documents and Terms 164 Removing stop words 165 Removing Punctuation and Noise 167 Trimming a DTM 170 Weighting a DTM 171 Advanced Representation of Text 172 n-grams 173 Collocations 174 Word Embeddings 176 Linguistic Preprocessing 177 Which Preprocessing to Use? 182 Text as Data 184 Deciding on the Right Method 185 Obtaining a Review Dataset 187 Dictionary Approaches to Text Analysis 189 Supervised Text Analysis: Automatic Classification and Sentiment Analysis 191 Putting Together a Workflow 191 Finding the Best Classifier 194 Using the Model 198 Deep Learning 199 Unsupervised Text Analysis: Topic Modeling 203 Latent Dirichlet Allocation (LDA) 203 Fitting an LDA Model 206 Analyzing Topic Model Results 207
Validating and Inspecting Topic Models 208 Beyond LDA 209 Automatic Analysis of Text ix
X ļ Contents 12.1 12.2 12.2.1 12.2.2 12.2.3 12.3 12.3.1 12.3.2 12.4 Scraping Online Data 212 Using Web APIs: From Open Resources to Twitter 213 Retrieving and Parsing Web Pages 219 Retrieving and Parsing an HTML Page 219 Crawling Websites 223 Dynamic Web Pages 225 Authentication, Cookies, and Sessions 228 Authentication and APIs 228 Authentication and Webpages 229 Ethical, Lega!, and Practical Considerations 230 13 Network Data 12 13.1 13.2 13.2.1 13.2.2 13.2.3 233 Representing and Visualizing Networks 234 Social Network Analysis 241 Paths and Reachability 242 Centrality Measures 246 Clustering and Community Detection 248 258 14.1 Beyond Text Analysis: Images, Audio and Video 259 14.2 Using Existing Libraries and APIs 261 14.3 Storing, Representing, and Converting Images 263 14.4 Image Classification 270 14.4.1 Basic Classification with Shallow Algorithms 272 14.4.2 Deep Learning for Image Analysis 273 14.4.3 Re-using an Open Source CNN 279 14 Multimedia Data 15 Scaling Up and Distributing 283 15.1 Storing Data in SQL and noSQL Databases 283 15.1.1 When to Use a Database 283 15.1.2 Choosing the Right Database 285 15.1.3 A Brief Example Using SQLite 286 15.2 Using Cloud Computing 286 15.3 Publishing Your Source 290 15.4 Distributing Your Software as Container 291 16 16.1 16.2 16.3 Where to Go Next 293 How Far Have We Come? 293 Where To Go Next? 294 Open, Transparent, and Ethical Computational Science 295 Bibliography 297 Index 303
A practicai introduction to the analysis of texts, networks, and code exampies in Python and R In disciplines such as political science, sociology, psychology, communication science, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computational linguistics, this accessible textbook teaches readers how to use state-of-the-art computational methods to perform data-driven analyses of social science issues. A team of authors with expertise in both the social sciences and computer science explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource՛ Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide a unified approach to the subject Includes sample code in Python and R and
links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Computational Analysis of Communication is an invaluable textbook and reference for students taking com putational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Atteveldt, Wouter van Trilling, Damian 1983- Arcíla Calderón, Carlos |
| author_GND | (DE-588)115041099X (DE-588)1253430918 (DE-588)106066335X |
| author_facet | Atteveldt, Wouter van Trilling, Damian 1983- Arcíla Calderón, Carlos |
| author_role | aut aut aut |
| author_sort | Atteveldt, Wouter van |
| author_variant | w v a wv wva d t dt c c a cc cca |
| building | Verbundindex |
| bvnumber | BV047643774 |
| classification_rvk | AP 15942 MR 5900 |
| ctrlnum | (OCoLC)1302320071 (DE-599)BVBBV047643774 |
| discipline | Allgemeines Soziologie |
| discipline_str_mv | Allgemeines Soziologie |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03954nam a2200541 c 4500</leader><controlfield tag="001">BV047643774</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20221012 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">211215s2022 a||| b||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781119680239</subfield><subfield code="c">Paperback</subfield><subfield code="9">978-1-119-68023-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781119680277</subfield><subfield code="c">Hardcover</subfield><subfield code="9">978-1-119-68027-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1119680239</subfield><subfield code="9">1119680239</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1302320071</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047643774</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-188</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-384</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-19</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">AP 15942</subfield><subfield code="0">(DE-625)6955:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MR 5900</subfield><subfield code="0">(DE-625)123525:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Atteveldt, Wouter van</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)115041099X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational analysis of communication</subfield><subfield code="b">a practical introduction to the analysis of texts, networks, and images with code examples in Python and R</subfield><subfield code="c">Wouter van Atteveldt, Vrije Universiteit Amsterdam, Damian Trilling, University of Amersterdam, Carlos Arcíla Calderón, University of Salamanca</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley Blackwell</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xiii, 314 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">"The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before"--</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Netzwerkanalyse</subfield><subfield code="g">Soziologie</subfield><subfield code="0">(DE-588)4205975-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Computerunterstützte Kommunikation</subfield><subfield code="0">(DE-588)4535905-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Social sciences / Network analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Communication / Network analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computational linguistics / Network analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Communication / Data processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Communication / Data processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Communication / Network analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computational linguistics / Network analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Social sciences / Network analysis</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Computerunterstützte Kommunikation</subfield><subfield code="0">(DE-588)4535905-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Netzwerkanalyse</subfield><subfield code="g">Soziologie</subfield><subfield code="0">(DE-588)4205975-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trilling, Damian</subfield><subfield code="d">1983-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1253430918</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arcíla Calderón, Carlos</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)106066335X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, PDF</subfield><subfield code="z">978-1-119-68027-7</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, EPUB</subfield><subfield code="z">978-1-119-68028-4</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Augsburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Augsburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Klappentext</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033027953</subfield></datafield></record></collection> |
| id | DE-604.BV047643774 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:48:13Z |
| indexdate | 2024-07-10T09:18:07Z |
| institution | BVB |
| isbn | 9781119680239 9781119680277 1119680239 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033027953 |
| oclc_num | 1302320071 |
| open_access_boolean | |
| owner | DE-188 DE-739 DE-384 DE-20 DE-19 DE-BY-UBM |
| owner_facet | DE-188 DE-739 DE-384 DE-20 DE-19 DE-BY-UBM |
| physical | xiii, 314 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Wiley Blackwell |
| record_format | marc |
| spelling | Atteveldt, Wouter van Verfasser (DE-588)115041099X aut Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R Wouter van Atteveldt, Vrije Universiteit Amsterdam, Damian Trilling, University of Amersterdam, Carlos Arcíla Calderón, University of Salamanca Hoboken, NJ Wiley Blackwell 2022 xiii, 314 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier "The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before"-- Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd rswk-swf Computerunterstützte Kommunikation (DE-588)4535905-2 gnd rswk-swf Social sciences / Network analysis Communication / Network analysis Computational linguistics / Network analysis Communication / Data processing Computerunterstützte Kommunikation (DE-588)4535905-2 s Netzwerkanalyse Soziologie (DE-588)4205975-6 s DE-604 Trilling, Damian 1983- Verfasser (DE-588)1253430918 aut Arcíla Calderón, Carlos Verfasser (DE-588)106066335X aut Erscheint auch als Online-Ausgabe, PDF 978-1-119-68027-7 Erscheint auch als Online-Ausgabe, EPUB 978-1-119-68028-4 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Atteveldt, Wouter van Trilling, Damian 1983- Arcíla Calderón, Carlos Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd Computerunterstützte Kommunikation (DE-588)4535905-2 gnd |
| subject_GND | (DE-588)4205975-6 (DE-588)4535905-2 |
| title | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R |
| title_auth | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R |
| title_exact_search | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R |
| title_exact_search_txtP | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R |
| title_full | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R Wouter van Atteveldt, Vrije Universiteit Amsterdam, Damian Trilling, University of Amersterdam, Carlos Arcíla Calderón, University of Salamanca |
| title_fullStr | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R Wouter van Atteveldt, Vrije Universiteit Amsterdam, Damian Trilling, University of Amersterdam, Carlos Arcíla Calderón, University of Salamanca |
| title_full_unstemmed | Computational analysis of communication a practical introduction to the analysis of texts, networks, and images with code examples in Python and R Wouter van Atteveldt, Vrije Universiteit Amsterdam, Damian Trilling, University of Amersterdam, Carlos Arcíla Calderón, University of Salamanca |
| title_short | Computational analysis of communication |
| title_sort | computational analysis of communication a practical introduction to the analysis of texts networks and images with code examples in python and r |
| title_sub | a practical introduction to the analysis of texts, networks, and images with code examples in Python and R |
| topic | Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd Computerunterstützte Kommunikation (DE-588)4535905-2 gnd |
| topic_facet | Netzwerkanalyse Soziologie Computerunterstützte Kommunikation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033027953&sequence=000001&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=033027953&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT atteveldtwoutervan computationalanalysisofcommunicationapracticalintroductiontotheanalysisoftextsnetworksandimageswithcodeexamplesinpythonandr AT trillingdamian computationalanalysisofcommunicationapracticalintroductiontotheanalysisoftextsnetworksandimageswithcodeexamplesinpythonandr AT arcilacalderoncarlos computationalanalysisofcommunicationapracticalintroductiontotheanalysisoftextsnetworksandimageswithcodeexamplesinpythonandr |