Handbook of Computational Social Science, Volume 2 (eBook, ePUB)

Data Science, Statistical Modelling, and Machine Learning Methods
Redaktion: Engel, Uwe; Lyberg, Lars; Liu, Sunny; Quan-Haase, Anabel

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 434
• Erscheinungstermin: 10. November 2021
• Englisch
• ISBN-13: 9781000448627
• Artikelnr.: 62906278

Autorenporträt

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.

Inhaltsangabe

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