Handbook of Computational Social Science, Volume 2
Data Science, Statistical Modelling, and Machine Learning Methods
Herausgeber: Engel, Uwe; Liu, Sunny Xun; Quan-Haase, Anabel
Handbook of Computational Social Science, Volume 2
Data Science, Statistical Modelling, and Machine Learning Methods
Herausgeber: Engel, Uwe; Liu, Sunny Xun; Quan-Haase, Anabel
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The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.
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The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 412
- Erscheinungstermin: 15. November 2021
- Englisch
- Abmessung: 244mm x 170mm x 24mm
- Gewicht: 889g
- ISBN-13: 9780367457808
- ISBN-10: 0367457806
- Artikelnr.: 62228600
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 412
- Erscheinungstermin: 15. November 2021
- Englisch
- Abmessung: 244mm x 170mm x 24mm
- Gewicht: 889g
- ISBN-13: 9780367457808
- ISBN-10: 0367457806
- Artikelnr.: 62228600
Uwe Engel is Professor at the University of Bremen, Germany, where he held a chair in sociology from 2000 to 2020. From 2008 to 2013, Dr. Engel coordinated the Priority Programme on "Survey Methodology" of the German Research Foundation. His current research focuses on data science, human-robot interaction, and opinion dynamics. Anabel Quan-Haase is Professor of Sociology and Information and Media Studies at Western University and Director of the SocioDigital Media Lab, London, Canada. Her research interests include social media, social networks, life course, social capital, computational social science, and digital inequality/inclusion. Sunny Xun Liu is a research scientist at Stanford Social Media Lab, USA. Her research focuses on the social and psychological effects of social media and AI, social media and well-being, and how the design of social robots impact psychological perceptions. Lars Lyberg was Head of the Research and Development Department at Statistics Sweden and Professor at Stockholm University. He was an elected member of the International Statistical Institute. In 2018, he received the AAPOR Award for Exceptionally Distinguished Achievement.
Preface
1. Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. Data in CSS: Collection, Management, and Cleaning
2. A Brief History of APIs: Limitations and Opportunities for Online
Research
Jakob Jünger
3. Application Programming Interfaces and Web Data For Social Research
Dominic Nyhuis
4. Web Data Mining: Collecting Textual Data from Web Pages Using R
Stefan Bosse, Lena Dahlhaus and Uwe Engel
5. Analyzing Data Streams for Social Scientists
Lianne Ippel, Maurits Kaptein and Jeroen Vermunt
6. Handling Missing Data in Large Data Bases
Martin Spiess and Thomas Augustin
7. A Primer on Probabilistic Record Linkage
Ted Enamorado
8. Reproducibility and Principled Data Processing
John McLevey, Pierson Browne and Tyler Crick
Section II. Data Quality in CSS Research
9. Applying a Total Error Framework for Digital Traces to Social Media
Research
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia
Wagner
10. Crowdsourcing in Observational and Experimental Research
Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
11. Inference from Probability and Nonprobability Samples
Rebecca Andridge and Richard Valliant
12. Challenges of Online Non-Probability Surveys
Jelke Bethlehem
Section III. Statistical Modelling and Simulation
13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile
Agents
Stefan Bosse
14. Agent-based Modelling for Cultural Networks: Tagging by Artificial
Intelligent Cultural Agents
Fernando Sancho-Caparrini and Juan Luis Suárez
15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify
Unusual Developmental Trajectories
Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian
Lemmerich
16. Disaggregation via Gaussian Regression for Robust Analysis of
Heterogeneous Data
Nazanin Alipourfard, Keith Burghardt and Kristina Lerman
Section IV: Machine Learning Methods
17. Machine Learning Methods for Computational Social Science
Richard D. De Veaux and Adam Eck
18. Principal Component Analysis
Andreas Pöge and Jost Reinecke
19. Unsupervised Methods: Clustering Methods
Johann Bacher, Andreas Pöge and Knut Wenzig
20. Text Mining and Topic Modeling
Raphael H. Heiberger and Sebastian Munoz-Najar Galvez
21. From Frequency Counts to Contextualized Word Embeddings: The
Saussurean Turn in Automatic Content Analysis
Gregor Wiedemann and Cornelia Fedtke
22. Automated Video Analysis for Social Science Research
Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and
Rainer Stiefelhagen
1. Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. Data in CSS: Collection, Management, and Cleaning
2. A Brief History of APIs: Limitations and Opportunities for Online
Research
Jakob Jünger
3. Application Programming Interfaces and Web Data For Social Research
Dominic Nyhuis
4. Web Data Mining: Collecting Textual Data from Web Pages Using R
Stefan Bosse, Lena Dahlhaus and Uwe Engel
5. Analyzing Data Streams for Social Scientists
Lianne Ippel, Maurits Kaptein and Jeroen Vermunt
6. Handling Missing Data in Large Data Bases
Martin Spiess and Thomas Augustin
7. A Primer on Probabilistic Record Linkage
Ted Enamorado
8. Reproducibility and Principled Data Processing
John McLevey, Pierson Browne and Tyler Crick
Section II. Data Quality in CSS Research
9. Applying a Total Error Framework for Digital Traces to Social Media
Research
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia
Wagner
10. Crowdsourcing in Observational and Experimental Research
Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
11. Inference from Probability and Nonprobability Samples
Rebecca Andridge and Richard Valliant
12. Challenges of Online Non-Probability Surveys
Jelke Bethlehem
Section III. Statistical Modelling and Simulation
13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile
Agents
Stefan Bosse
14. Agent-based Modelling for Cultural Networks: Tagging by Artificial
Intelligent Cultural Agents
Fernando Sancho-Caparrini and Juan Luis Suárez
15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify
Unusual Developmental Trajectories
Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian
Lemmerich
16. Disaggregation via Gaussian Regression for Robust Analysis of
Heterogeneous Data
Nazanin Alipourfard, Keith Burghardt and Kristina Lerman
Section IV: Machine Learning Methods
17. Machine Learning Methods for Computational Social Science
Richard D. De Veaux and Adam Eck
18. Principal Component Analysis
Andreas Pöge and Jost Reinecke
19. Unsupervised Methods: Clustering Methods
Johann Bacher, Andreas Pöge and Knut Wenzig
20. Text Mining and Topic Modeling
Raphael H. Heiberger and Sebastian Munoz-Najar Galvez
21. From Frequency Counts to Contextualized Word Embeddings: The
Saussurean Turn in Automatic Content Analysis
Gregor Wiedemann and Cornelia Fedtke
22. Automated Video Analysis for Social Science Research
Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and
Rainer Stiefelhagen
Preface
1. Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. Data in CSS: Collection, Management, and Cleaning
2. A Brief History of APIs: Limitations and Opportunities for Online
Research
Jakob Jünger
3. Application Programming Interfaces and Web Data For Social Research
Dominic Nyhuis
4. Web Data Mining: Collecting Textual Data from Web Pages Using R
Stefan Bosse, Lena Dahlhaus and Uwe Engel
5. Analyzing Data Streams for Social Scientists
Lianne Ippel, Maurits Kaptein and Jeroen Vermunt
6. Handling Missing Data in Large Data Bases
Martin Spiess and Thomas Augustin
7. A Primer on Probabilistic Record Linkage
Ted Enamorado
8. Reproducibility and Principled Data Processing
John McLevey, Pierson Browne and Tyler Crick
Section II. Data Quality in CSS Research
9. Applying a Total Error Framework for Digital Traces to Social Media
Research
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia
Wagner
10. Crowdsourcing in Observational and Experimental Research
Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
11. Inference from Probability and Nonprobability Samples
Rebecca Andridge and Richard Valliant
12. Challenges of Online Non-Probability Surveys
Jelke Bethlehem
Section III. Statistical Modelling and Simulation
13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile
Agents
Stefan Bosse
14. Agent-based Modelling for Cultural Networks: Tagging by Artificial
Intelligent Cultural Agents
Fernando Sancho-Caparrini and Juan Luis Suárez
15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify
Unusual Developmental Trajectories
Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian
Lemmerich
16. Disaggregation via Gaussian Regression for Robust Analysis of
Heterogeneous Data
Nazanin Alipourfard, Keith Burghardt and Kristina Lerman
Section IV: Machine Learning Methods
17. Machine Learning Methods for Computational Social Science
Richard D. De Veaux and Adam Eck
18. Principal Component Analysis
Andreas Pöge and Jost Reinecke
19. Unsupervised Methods: Clustering Methods
Johann Bacher, Andreas Pöge and Knut Wenzig
20. Text Mining and Topic Modeling
Raphael H. Heiberger and Sebastian Munoz-Najar Galvez
21. From Frequency Counts to Contextualized Word Embeddings: The
Saussurean Turn in Automatic Content Analysis
Gregor Wiedemann and Cornelia Fedtke
22. Automated Video Analysis for Social Science Research
Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and
Rainer Stiefelhagen
1. Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. Data in CSS: Collection, Management, and Cleaning
2. A Brief History of APIs: Limitations and Opportunities for Online
Research
Jakob Jünger
3. Application Programming Interfaces and Web Data For Social Research
Dominic Nyhuis
4. Web Data Mining: Collecting Textual Data from Web Pages Using R
Stefan Bosse, Lena Dahlhaus and Uwe Engel
5. Analyzing Data Streams for Social Scientists
Lianne Ippel, Maurits Kaptein and Jeroen Vermunt
6. Handling Missing Data in Large Data Bases
Martin Spiess and Thomas Augustin
7. A Primer on Probabilistic Record Linkage
Ted Enamorado
8. Reproducibility and Principled Data Processing
John McLevey, Pierson Browne and Tyler Crick
Section II. Data Quality in CSS Research
9. Applying a Total Error Framework for Digital Traces to Social Media
Research
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia
Wagner
10. Crowdsourcing in Observational and Experimental Research
Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
11. Inference from Probability and Nonprobability Samples
Rebecca Andridge and Richard Valliant
12. Challenges of Online Non-Probability Surveys
Jelke Bethlehem
Section III. Statistical Modelling and Simulation
13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile
Agents
Stefan Bosse
14. Agent-based Modelling for Cultural Networks: Tagging by Artificial
Intelligent Cultural Agents
Fernando Sancho-Caparrini and Juan Luis Suárez
15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify
Unusual Developmental Trajectories
Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian
Lemmerich
16. Disaggregation via Gaussian Regression for Robust Analysis of
Heterogeneous Data
Nazanin Alipourfard, Keith Burghardt and Kristina Lerman
Section IV: Machine Learning Methods
17. Machine Learning Methods for Computational Social Science
Richard D. De Veaux and Adam Eck
18. Principal Component Analysis
Andreas Pöge and Jost Reinecke
19. Unsupervised Methods: Clustering Methods
Johann Bacher, Andreas Pöge and Knut Wenzig
20. Text Mining and Topic Modeling
Raphael H. Heiberger and Sebastian Munoz-Najar Galvez
21. From Frequency Counts to Contextualized Word Embeddings: The
Saussurean Turn in Automatic Content Analysis
Gregor Wiedemann and Cornelia Fedtke
22. Automated Video Analysis for Social Science Research
Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and
Rainer Stiefelhagen