Main description:

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.

Contents:

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