CiNii 図書 - Data science, statistical modelling, and machine learning methods

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks. 4. Covers important and emergent topics in the field like the relationship between CSS, AI and machine learning.

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 Junger 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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Data science, statistical modelling, and machine learning methods

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