Handbook of Computational Social Science, Volume 2

Preface

1. 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

2. A Brief History of APIs: Limitations and Opportunities for Online Research

Jakob Junger

3. Application Programming Interfaces and Web Data For Social Research

Dominic Nyhuis

4. Web Data Mining: Collecting Textual Data from Web Pages Using R

Stefan Bosse, Lena Dahlhaus and Uwe Engel

5. Analyzing Data Streams for Social Scientists

Lianne Ippel, Maurits Kaptein and Jeroen Vermunt

6. Handling Missing Data in Large Data Bases

Martin Spiess and Thomas Augustin

7. A Primer on Probabilistic Record Linkage

Ted Enamorado

8. Reproducibility and Principled Data Processing

John McLevey, Pierson Browne and Tyler Crick

Section II. Data Quality in CSS Research

9. Applying a Total Error Framework for Digital Traces to Social Media Research

Indira Sen, Fabian Flock, Katrin Weller, Bernd Weiß and Claudia Wagner

10. Crowdsourcing in Observational and Experimental Research

Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso

11. Inference from Probability and Nonprobability Samples

Rebecca Andridge and Richard Valliant

12. Challenges of Online Non-Probability Surveys

Jelke Bethlehem

Section III. Statistical Modelling and Simulation

13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile Agents

Stefan Bosse

14. Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural Agents

Fernando Sancho-Caparrini and Juan Luis Suarez

15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental Trajectories

Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich

16. Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data

Nazanin Alipourfard, Keith Burghardt and Kristina Lerman

Section IV: Machine Learning Methods

17. Machine Learning Methods for Computational Social Science

Richard D. De Veaux and Adam Eck

18. Principal Component Analysis

Andreas Poge and Jost Reinecke

19. Unsupervised Methods: Clustering Methods

Johann Bacher, Andreas Poge and Knut Wenzig

20. Text Mining and Topic Modeling

Raphael H. Heiberger and Sebastian Munoz-Najar Galvez

21. From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content Analysis

Gregor Wiedemann and Cornelia Fedtke

22. Automated Video Analysis for Social Science Research

Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen