1st Edition

Handbook of Computational Social Science, Volume 2 Data Science, Statistical Modelling, and Machine Learning Methods

Edited By Uwe Engel, Anabel Quan-Haase, Sunny Liu, Lars E Lyberg Copyright 2022
    434 Pages 102 B/W Illustrations
    by Routledge

    434 Pages 102 B/W Illustrations
    by Routledge

    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.


    1. Introduction to the Handbook of Computational Social Science
    2. Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg

      Section I. Data in CSS: Collection, Management, and Cleaning

    3. A Brief History of APIs: Limitations and Opportunities for Online Research
    4. Jakob Jünger

    5. Application Programming Interfaces and Web Data For Social Research
    6. Dominic Nyhuis

    7. Web Data Mining: Collecting Textual Data from Web Pages Using R
    8. Stefan Bosse, Lena Dahlhaus and Uwe Engel

    9. Analyzing Data Streams for Social Scientists
    10. Lianne Ippel, Maurits Kaptein and Jeroen Vermunt

    11. Handling Missing Data in Large Data Bases
    12. Martin Spiess and Thomas Augustin

    13. Probabilistic Record Linkage in R
    14. Ted Enamorado

    15. Reproducibility and Principled Data Processing
    16. John McLevey, Pierson Browne and Tyler Crick

      Section II. Data Quality in CSS Research

    17. Applying a Total Error Framework for Digital Traces to Social Media Research
    18. Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia Wagner

    19. Crowdsourcing in Observational and Experimental Research
    20. Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso

    21. Inference from Probability and Non-Probability Samples
    22. Rebecca Andridge and Richard Valliant

    23. Challenges of Online Non-Probability Surveys
    24. Jelke Bethlehem

      Section III. Statistical Modelling and Simulation

    25. Large-scale Agent-based Simulation and Crowd Sensing with Mobile Agents
    26. Stefan Bosse

    27. Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural Agents
    28. Fernando Sancho-Caparrini and Juan Luis Suárez

    29. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental Trajectories
    30. Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich

    31. Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data
    32. Nazanin Alipourfard, Keith Burghardt and Kristina Lerman

      Section IV: Machine Learning Methods

    33. Machine Learning Methods for Computational Social Science
    34. Richard D. De Veaux and Adam Eck

    35. Principal Component Analysis
    36. Andreas Pöge and Jost Reinecke

    37. Unsupervised Methods: Clustering Methods
    38. Johann Bacher, Andreas Pöge and Knut Wenzig

    39. Text Mining and Topic Modeling
    40. Raphael H. Heiberger and Sebastian Munoz-Najar Galvez

    41. From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content Analysis
    42. Gregor Wiedemann and Cornelia Fedtke

    43. Automated Video Analysis for Social Science Research

             Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen


    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.