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Handbook of Computational Social Science - Vol 1 & Vol 2 book cover

1st Edition

Handbook of Computational Social Science - Vol 1 & Vol 2

Edited By Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg
    848 Pages
    by Routledge

    848 Pages
    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. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.

    The 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.

    Volume 1

    Preface

    1. Introduction to the Handbook of Computational Social Science

      2. Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg

      Section I. The Scope and Boundaries of CSS

    2. The Scope of Computational Social Science

      3. Claudio Cioffi-Revilla

    3. Analytical Sociology amidst a Computational Social Science Revolution

      4. Benjamin F. Jarvis, Marc Keuschnigg and Peter Hedström

    4. Computational Cognitive Modeling in the Social Sciences

      5. Holger Schultheis

    5. Computational Communication Science: Lessons from Working Group Sessions with Experts of an Emerging Research Field

      6. Stephanie Geise and Annie Waldherr

    6. A Changing Survey Landscape

      7. Lars Lyberg and Steven G. Heeringa

    7. Digital Trace Data: Modes of Data Collection, Applications, and Errors at a Glance

      8. Florian Keusch and Frauke Kreuter

    8. Open Computational Social Science

      9. Jan G. Voelkel and Jeremy Freese

    9. Causal and Predictive Modeling in Computational Social Science

      10. Uwe Engel

    10. Data-driven Agent-based Modeling in Computational Social Science

      11. Jan Lorenz

      Section II. Privacy, Ethics, and Politics in CSS Research

    11. Ethics and Privacy in Computational Social Science: A Call for Pedagogy

      12. William Hollingshead, Anabel Quan-Haase and Wenhong Chen

    12. Deliberating with the Public: An Agenda to Include Stakeholder Input on Municipal "Big Data" Projects

      13. James Popham, Jennifer Lavoie, Andrea Corradi and Nicole Coomber

    13. Analysis of the Principled-AI Framework´s Constraints in Becoming a Methodological Reference for Trustworthy-AI Design

      14. Daniel Varona and Juan Luis Suarez

      Section III. Case Studies and Research Examples

    14. Sensing Close-Range Proximity for Studying Face-to-Face Interaction

      15. Johann Schaible, Marcos Oliveira, Maria Zens and Mathieu Génois

    15. Social Media Data in Affective Science

      16. Max Pellert, Simon Schweighofer and David Garcia

    16. Understanding Political Sentiment: Using Twitter to Map the US 2016 Democratic Primaries

      17. Niklas M Loynes and Mark J Elliot

    17. The Social Influence of Bots and Trolls in Social Media

      18. Yimin Chen

    18. Social Bots and Social Media Manipulation in 2020: The Year in Review

      19. Ho-Chun Herbert Chang, Emily Chen, Meiqing Zhang, Goran Muric, and Emilio Ferrara

    19. A Picture is (still) Worth a Thousand Words: The Impact of Appearance and Characteristic Narratives on People’s Perceptions of Social Robots

      20. Sunny Xun Liu, Elizabeth Arredondo, Hannah Miezkowski, Jeff Hancock and Byron Reeves

    20. Data Quality and Privacy Concerns in Digital Trace Data: Insights from a Delphi Study on Machine Learning and Robots in Human Life

      21. Uwe Engel and Lena Dahlhaus

    21. Effective Fight Against Extremist Discourse On-Line: The Case of ISIS’s Propaganda

      22. Séraphin Alava and Rasha Nagem

    22. Public Opinion Formation on the Far Right

              Michael Adelmund and Uwe Engel

    Volume 2

    Preface

    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

    2. A Brief History of APIs: Limitations and Opportunities for Online Research

      3. Jakob Jünger

    3. Application Programming Interfaces and Web Data For Social Research

      4. Dominic Nyhuis

    4. Web Data Mining: Collecting Textual Data from Web Pages Using R

      5. Stefan Bosse, Lena Dahlhaus and Uwe Engel

    5. Analyzing Data Streams for Social Scientists

      6. Lianne Ippel, Maurits Kaptein and Jeroen Vermunt

    6. Handling Missing Data in Large Data Bases

      7. Martin Spiess and Thomas Augustin

    7. Probabilistic Record Linkage in R

      8. Ted Enamorado

    8. Reproducibility and Principled Data Processing

      9. 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

      10. Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia Wagner

    10. Crowdsourcing in Observational and Experimental Research

      11. Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso

    11. Inference from Probability and Non-Probability Samples

      12. Rebecca Andridge and Richard Valliant

    12. Challenges of Online Non-Probability Surveys

      13. Jelke Bethlehem

      Section III. Statistical Modelling and Simulation

    13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile Agents

      14. Stefan Bosse

    14. Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural Agents

      15. Fernando Sancho-Caparrini and Juan Luis Suárez

    15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental Trajectories

      16. Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich

    16. Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data

      17. Nazanin Alipourfard, Keith Burghardt and Kristina Lerman

      Section IV: Machine Learning Methods

    17. Machine Learning Methods for Computational Social Science

      18. Richard D. De Veaux and Adam Eck

    18. Principal Component Analysis

      19. Andreas Pöge and Jost Reinecke

    19. Unsupervised Methods: Clustering Methods

      20. Johann Bacher, Andreas Pöge and Knut Wenzig

    20. Text Mining and Topic Modeling

      21. Raphael H. Heiberger and Sebastian Munoz-Najar Galvez

    21. From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content Analysis

      22. Gregor Wiedemann and Cornelia Fedtke

    22. Automated Video Analysis for Social Science Research

              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.

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