Handbook of Computational Social Science, Volume 2

Data Science, Statistical Modelling, and Machine Learning Methods
1. Auflage 2021
ISBN: 978-0-367-45780-8
Verlag: Routledge

Buch, Englisch, 434 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1100 g

Reihe: European Association of Methodology Series

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.

Zielgruppe

Postgraduate and Professional

Autoren/Hrsg.

Engel, Uwe

Quan-Haase, Anabel

Liu, Sunny Xun

Lyberg, Lars

Liu, Sunny

Fachgebiete

• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden
• Mathematik | Informatik EDV | Informatik Informatik

Inhaltsverzeichnis

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 Jünger

- 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 Flöck, 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 Suárez

- 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 Pöge and Jost Reinecke

- Unsupervised Methods: Clustering Methods

Johann Bacher, Andreas Pöge 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

Über Autor(innen)

Uwe Engel is Professor at the University of Bremen, Germany, where he held a chair in sociology from 2000 to 2020. From 2008 to 2013, Dr. Engel coordinated the Priority Programme on “Survey Methodology” of the German Research Foundation. His current research focuses on data science, human-robot interaction, and opinion dynamics.

Anabel Quan-Haase is Professor of Sociology and Information and Media Studies at Western University and Director of the SocioDigital Media Lab, London, Canada. Her research interests include social media, social networks, life course, social capital, computational social science, and digital inequality/inclusion.

Sunny Xun Liu is a research scientist at Stanford Social Media Lab, USA. Her research focuses on the social and psychological effects of social media and AI, social media and well-being, and how the design of social robots impact psychological perceptions.

Lars Lyberg was Head of the Research and Development Department at Statistics Sweden and Professor at Stockholm University. He was an elected member of the International Statistical Institute. In 2018, he received the AAPOR Award for Exceptionally Distinguished Achievement.