Handbook of Computational Social Science, 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 Juenger




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




A Primer on Probabilistic Record Linkage


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 Floeck, 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 Nonprobability 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 Suarez




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 Poege and Jost Reinecke




Unsupervised Methods: Clustering Methods
Johann Bacher, Andreas Poege 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

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• Neurociências
• Ergonómica
• Pesquisa e estatística social
• Ensaios e medições psicológicas
• Métodos de pesquisa: geral

survey data;data analysis;data science;information technology;AI;socio-robotics;quantitative;survey methodology;ethics;ethical standards;privacy;replication;politics;survey design;social media;big data;social;human-robot interaction;machine learning;open data;data archives;data ownership;digital trace;unstructured data;Latent Dirichlet Allocation;National Educational Panel Study;BDI Model;Dropout Intention;Probabilistic Record Linkage;Deep Neural Network Model;Supervised Machine Learning;Social Science Research;Data Set;Computational Social Science;Crowd Sensing;Correlation PCA;ABM;Web Scraping;Web Data Mining;Dtd;Web API;Html Document;General Data Protection Regulation;Stochastic Gradient Descent;Vice Versa;Digital Twins;Automated Video Analysis;Nonprobability Samples;MTurk Workers