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HANDBOOK OF COMPUTATIONAL SOCIAL SCIENCE, VOLUME 2. DATA SCIENCE, STATISTICAL MODELLING...
Título:
HANDBOOK OF COMPUTATIONAL SOCIAL SCIENCE, VOLUME 2. DATA SCIENCE, STATISTICAL MODELLING...
Subtítulo:
Autor:
ENGEL, U
Editorial:
ROUTLEDGE
Año de edición:
2021
ISBN:
978-1-032-07770-3
Páginas:
434
92,04 €

 

Sinopsis

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.

Table of 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 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

Probabilistic Record Linkage in R

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 Non-Probability 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