Handbook of Computational Social Science, Volume 2

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.

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.

The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.

With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

Autorentext

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.

Klappentext

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. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

Inhalt

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