Handbook of Computational Social Science, Volume 2

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.

• Preface Introduction to the Handbook of Computational Social Science Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars LybergSection I. Data in CSS: Collection, Management, and Cleaning A Brief History of APIs: Limitations and Opportunities for Online ResearchJakob Jünger Application Programming Interfaces and Web Data For Social Research Dominic Nyhuis Web Data Mining: Collecting Textual Data from Web Pages Using RStefan Bosse, Lena Dahlhaus and Uwe Engel Analyzing Data Streams for Social ScientistsLianne Ippel, Maurits Kaptein and Jeroen Vermunt Handling Missing Data in Large Data BasesMartin Spiess and Thomas Augustin A Primer on Probabilistic Record Linkage Ted Enamorado Reproducibility and Principled Data ProcessingJohn McLevey, Pierson Browne and Tyler CrickSection II. Data Quality in CSS Research Applying a Total Error Framework for Digital Traces to Social Media ResearchIndira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia Wagner Crowdsourcing in Observational and Experimental ResearchCamilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso Inference from Probability and Nonprobability SamplesRebecca Andridge and Richard Valliant Challenges of Online Non-Probability SurveysJelke BethlehemSection 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 AgentsFernando Sancho-Caparrini and Juan Luis Suárez Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental TrajectoriesAxel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous DataNazanin Alipourfard, Keith Burghardt and Kristina LermanSection IV: Machine Learning Methods Machine Learning Methods for Computational Social ScienceRichard D. De Veaux and Adam Eck Principal Component AnalysisAndreas Pöge and Jost Reinecke Unsupervised Methods: Clustering MethodsJohann Bacher, Andreas Pöge and Knut Wenzig Text Mining and Topic ModelingRaphael H. Heiberger and Sebastian Munoz-Najar Galvez From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content AnalysisGregor Wiedemann and Cornelia Fedtke Automated Video Analysis for Social Science Research Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen