1st Edition
Handbook of Computational Social Science, Volume 2 Data Science, Statistical Modelling, and Machine Learning Methods
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.
Preface
- Introduction to the Handbook of Computational Social Science
- A Brief History of APIs: Limitations and Opportunities for Online Research
- Application Programming Interfaces and Web Data For Social Research
- Web Data Mining: Collecting Textual Data from Web Pages Using R
- Analyzing Data Streams for Social Scientists
- Handling Missing Data in Large Data Bases
- Probabilistic Record Linkage in R
- Reproducibility and Principled Data Processing
- Applying a Total Error Framework for Digital Traces to Social Media Research
- Crowdsourcing in Observational and Experimental Research
- Inference from Probability and Non-Probability Samples
- Challenges of Online Non-Probability Surveys
- Large-scale Agent-based Simulation and Crowd Sensing with Mobile Agents
- Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural Agents
- Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental Trajectories
- Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data
- Machine Learning Methods for Computational Social Science
- Principal Component Analysis
- Unsupervised Methods: Clustering Methods
- Text Mining and Topic Modeling
- From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content Analysis
- Automated Video Analysis for Social Science Research
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. Data in CSS: Collection, Management, and Cleaning
Jakob Jünger
Dominic Nyhuis
Stefan Bosse, Lena Dahlhaus and Uwe Engel
Lianne Ippel, Maurits Kaptein and Jeroen Vermunt
Martin Spiess and Thomas Augustin
Ted Enamorado
John McLevey, Pierson Browne and Tyler Crick
Section II. Data Quality in CSS Research
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia Wagner
Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
Rebecca Andridge and Richard Valliant
Jelke Bethlehem
Section III. Statistical Modelling and Simulation
Stefan Bosse
Fernando Sancho-Caparrini and Juan Luis Suárez
Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich
Nazanin Alipourfard, Keith Burghardt and Kristina Lerman
Section IV: Machine Learning Methods
Richard D. De Veaux and Adam Eck
Andreas Pöge and Jost Reinecke
Johann Bacher, Andreas Pöge and Knut Wenzig
Raphael H. Heiberger and Sebastian Munoz-Najar Galvez
Gregor Wiedemann and Cornelia Fedtke
Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen
Biography
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.
