Handbook of Computational Social Science, Volume 1
Theory, Case Studies and Ethics
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 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 in 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 scientifi c and engineering sectors.
Table of Contents
- Introduction to the Handbook of Computational Social Science
- The Scope of Computational Social Science
- Analytical Sociology amidst a Computational Social Science Revolution
- Computational Cognitive Modeling in the Social Sciences
- Computational Communication Science: Lessons from Working Group Sessions with Experts of an Emerging Research Field
- A Changing Survey Landscape
- Digital Trace Data: Modes of Data Collection, Applications, and Errors at a Glance
- Open Computational Social Science
- Causal and Predictive Modeling in Computational Social Science
- Data-driven Agent-based Modeling in Computational Social Science
- Ethics and Privacy in Computational Social Science: A Call for Pedagogy
- Deliberating with the Public: An Agenda to Include Stakeholder Input on Municipal "Big Data" Projects
- Analysis of the Principled-AI Framework´s Constraints in Becoming a Methodological Reference for Trustworthy-AI Design
- Sensing Close-Range Proximity for Studying Face-to-Face Interaction
- Social Media Data in Affective Science
- Understanding Political Sentiment: Using Twitter to Map the US 2016 Democratic Primaries
- The Social Influence of Bots and Trolls in Social Media
- Social Bots and Social Media Manipulation in 2020: The Year in Review
- A Picture is (still) Worth a Thousand Words: The Impact of Appearance and Characteristic Narratives on People’s Perceptions of Social Robots
- Data Quality and Privacy Concerns in Digital Trace Data: Insights from a Delphi Study on Machine Learning and Robots in Human Life
- Effective Fight Against Extremist Discourse On-Line: The Case of ISIS’s Propaganda
- Public Opinion Formation on the Far Right
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. The Scope and Boundaries of CSS
Benjamin F. Jarvis, Marc Keuschnigg and Peter Hedström
Stephanie Geise and Annie Waldherr
Lars Lyberg and Steven G. Heeringa
Florian Keusch and Frauke Kreuter
Jan G. Voelkel and Jeremy Freese
Section II. Privacy, Ethics, and Politics in CSS Research
William Hollingshead, Anabel Quan-Haase and Wenhong Chen
James Popham, Jennifer Lavoie, Andrea Corradi and Nicole Coomber
Daniel Varona and Juan Luis Suarez
Section III. Case Studies and Research Examples
Johann Schaible, Marcos Oliveira, Maria Zens and Mathieu Génois
Max Pellert, Simon Schweighofer and David Garcia
Niklas M Loynes and Mark J Elliot
Ho-Chun Herbert Chang, Emily Chen, Meiqing Zhang, Goran Muric, and Emilio Ferrara
Sunny Xun Liu, Elizabeth Arredondo, Hannah Miezkowski, Jeff Hancock and Byron Reeves
Uwe Engel and Lena Dahlhaus
Séraphin Alava and Rasha Nagem
Michael Adelmund and Uwe Engel
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 e- ects of social media and AI, social media and well-being, and how the design of social robots impacts 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.