Handbook of Computational Social Science - Vol 1 & Vol 2

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.

Table of Contents:
Volume 1 Preface Introduction to the Handbook of Computational Social Science Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg Section I. The Scope and Boundaries of CSS The Scope of Computational Social Science Claudio Cioffi-Revilla Analytical Sociology amidst a Computational Social Science Revolution Benjamin F. Jarvis, Marc Keuschnigg and Peter Hedström Computational Cognitive Modeling in the Social Sciences Holger Schultheis Computational Communication Science: Lessons from Working Group Sessions with Experts of an Emerging Research Field Stephanie Geise and Annie Waldherr A Changing Survey Landscape Lars Lyberg and Steven G. Heeringa Digital Trace Data: Modes of Data Collection, Applications, and Errors at a Glance Florian Keusch and Frauke Kreuter Open Computational Social Science Jan G. Voelkel and Jeremy Freese Causal and Predictive Modeling in Computational Social Science Uwe Engel Data-driven Agent-based Modeling in Computational Social Science Jan Lorenz Section II. Privacy, Ethics, and Politics in CSS Research Ethics and Privacy in Computational Social Science: A Call for Pedagogy William Hollingshead, Anabel Quan-Haase and Wenhong Chen Deliberating with the Public: An Agenda to Include Stakeholder Input on Municipal "Big Data" Projects James Popham, Jennifer Lavoie, Andrea Corradi and Nicole Coomber Analysis of the Principled-AI Framework´s Constraints in Becoming a Methodological Reference for Trustworthy-AI Design Daniel Varona and Juan Luis Suarez Section III. Case Studies and Research Examples Sensing Close-Range Proximity for Studying Face-to-Face Interaction Johann Schaible, Marcos Oliveira, Maria Zens and Mathieu Génois Social Media Data in Affective Science Max Pellert, Simon Schweighofer and David Garcia Understanding Political Sentiment: Using Twitter to Map the US 2016 Democratic Primaries Niklas M Loynes and Mark J Elliot The Social Influence of Bots and Trolls in Social Media Yimin Chen Social Bots and Social Media Manipulation in 2020: The Year in Review Ho-Chun Herbert Chang, Emily Chen, Meiqing Zhang, Goran Muric, and Emilio Ferrara A Picture is (still) Worth a Thousand Words: The Impact of Appearance and Characteristic Narratives on People’s Perceptions of Social Robots Sunny Xun Liu, Elizabeth Arredondo, Hannah Miezkowski, Jeff Hancock and Byron Reeves Data Quality and Privacy Concerns in Digital Trace Data: Insights from a Delphi Study on Machine Learning and Robots in Human Life Uwe Engel and Lena Dahlhaus Effective Fight Against Extremist Discourse On-Line: The Case of ISIS’s Propaganda Séraphin Alava and Rasha Nagem Public Opinion Formation on the Far Right Michael Adelmund and Uwe Engel Volume 2 Preface 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 A Brief History of APIs: Limitations and Opportunities for Online Research Jakob Jünger Application Programming Interfaces and Web Data For Social Research Dominic Nyhuis Web Data Mining: Collecting Textual Data from Web Pages Using R Stefan Bosse, Lena Dahlhaus and Uwe Engel Analyzing Data Streams for Social Scientists Lianne Ippel, Maurits Kaptein and Jeroen Vermunt Handling Missing Data in Large Data Bases Martin Spiess and Thomas Augustin Probabilistic Record Linkage in R Ted Enamorado Reproducibility and Principled Data Processing John McLevey, Pierson Browne and Tyler Crick Section II. Data Quality in CSS Research Applying a Total Error Framework for Digital Traces to Social Media Research Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia Wagner Crowdsourcing in Observational and Experimental Research Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso Inference from Probability and Non-Probability Samples Rebecca Andridge and Richard Valliant Challenges of Online Non-Probability Surveys Jelke Bethlehem Section III. Statistical Modelling and Simulation Large-scale Agent-based Simulation and Crowd Sensing with Mobile Agents Stefan Bosse Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural Agents Fernando Sancho-Caparrini and Juan Luis Suárez Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental Trajectories Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data Nazanin Alipourfard, Keith Burghardt and Kristina Lerman Section IV: Machine Learning Methods Machine Learning Methods for Computational Social Science Richard D. De Veaux and Adam Eck Principal Component Analysis Andreas Pöge and Jost Reinecke Unsupervised Methods: Clustering Methods Johann Bacher, Andreas Pöge and Knut Wenzig Text Mining and Topic Modeling Raphael H. Heiberger and Sebastian Munoz-Najar Galvez From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content Analysis Gregor Wiedemann and Cornelia Fedtke Automated Video Analysis for Social Science Research Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen