The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.

The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.

With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

chapter 1|13 pages

Introduction to The Handbook of Computational Social Science

ByUwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg

section Section I|110 pages

Data in CSS

chapter 2|16 pages

A Brief History of APIs

Limitations and opportunities for online research
ByJakob Jünger
Size: 1.39 MB

chapter 3|13 pages

Application Programming Interfaces and Web Data for Social Research

ByDominic Nyhuis

chapter 4|25 pages

Web Data Mining 1

Collecting textual data from web pages using R
ByStefan Bosse, Lena Dahlhaus, Uwe Engel

chapter 5|11 pages

Analyzing Data Streams for Social Scientists

ByLianne Ippel, Maurits Kaptein, Jeroen K. Vermunt

chapter 6|13 pages

Handling Missing Data in Large Databases

ByMartin Spiess, Thomas Augustin

chapter 7|13 pages

A Primer on Probabilistic Record Linkage

ByTed Enamorado

chapter 8|17 pages

Reproducibility and Principled Data Processing

ByJohn McLevey, Pierson Browne, Tyler Crick

section Section II|71 pages

Data quality in CSS research

chapter 9|13 pages

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

ByIndira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß, Claudia Wagner

chapter 10|18 pages

Crowdsourcing in Observational and Experimental Research

ByCamilla Zallot, Gabriele Paolacci, Jesse Chandler, Itay Sisso

chapter 11|23 pages

Inference from Probability and Nonprobability Samples

ByRebecca Andridge, Richard Valliant

chapter 12|15 pages

Challenges of Online Non-Probability Surveys

ByJelke Bethlehem

section Section III|92 pages

Statistical modelling and simulation

chapter 14|15 pages

Agent-Based Modelling for Cultural Networks

Tagging by artificial intelligent cultural agents
ByFernando Sancho-Caparrini, Juan Luis Suárez

chapter 15|25 pages

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

ByAxel Mayer, Christoph Kiefer, Benedikt Langenberg, Florian Lemmerich

chapter 16|20 pages

Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data

ByNazanin Alipourfard, Keith Burghardt, Kristina Lerman

part Section IV|110 pages

Machine learning methods

chapter 17|31 pages

Machine Learning Methods for Computational Social Science

ByRichard D. De Veaux, Adam Eck

chapter 18|12 pages

Principal Component Analysis

ByAndreas Pöge, Jost Reinecke

chapter 19|18 pages

Unsupervised Methods

Clustering methods
ByJohann Bacher, Andreas Pöge, Knut Wenzig
Size: 1.32 MB

chapter 20|14 pages

Text Mining and Topic Modeling

ByRaphael H. Heiberger, Sebastian Munoz-Najar Galvez

chapter 21|20 pages

From Frequency Counts to Contextualized Word Embeddings

The Saussurean turn in automatic content analysis
ByGregor Wiedemann, Cornelia Fedtke
Size: 1.38 MB

chapter 22|13 pages

Automated Video Analysis for Social Science Research 1

ByDominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend, Rainer Stiefelhagen