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Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods de Uwe Engel

Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods: European Association of Methodology Series

Editat de Uwe Engel, Anabel Quan-Haase, Sunny Liu, Lars Lyberg
en Limba Engleză Paperback – 17 noi 2021

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 scientifi c and engineering sectors.

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Specificații

ISBN-13: 9781032077703
ISBN-10: 1032077700
Pagini: 434
Ilustrații: 33 Tables, black and white; 102 Line drawings, black and white; 102 Illustrations, black and white
Dimensiuni: 174 x 246 x 45 mm
Greutate: 0.7 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Seria European Association of Methodology Series

Locul publicării:Oxford, United Kingdom

Public țintă

Postgraduate and Professional

Cuprins

Preface
  1. Introduction to the Handbook of Computational Social ScienceUwe 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 ResearchJakob Jünger
  3. Application Programming Interfaces and Web Data For Social ResearchDominic Nyhuis
  4. Web Data Mining: Collecting Textual Data from Web Pages Using RStefan Bosse, Lena Dahlhaus and Uwe Engel
  5. Analyzing Data Streams for Social ScientistsLianne Ippel, Maurits Kaptein and Jeroen Vermunt
  6. Handling Missing Data in Large Data BasesMartin Spiess and Thomas Augustin
  7. A Primer on Probabilistic Record Linkage
    Ted Enamorado
  8. Reproducibility and Principled Data ProcessingJohn 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 ResearchIndira Sen, Fabian Flöck, Katrin Weller, Bernd Weiß and Claudia Wagner
  10. Crowdsourcing in Observational and Experimental ResearchCamilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
  11. Inference from Probability and Nonprobability SamplesRebecca Andridge and Richard Valliant
  12. Challenges of Online Non-Probability SurveysJelke Bethlehem
    Section III. Statistical Modelling and Simulation
  13. Large-scale Agent-based Simulation and Crowd Sensing with Mobile AgentsStefan Bosse
  14. Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural AgentsFernando Sancho-Caparrini and Juan Luis Suárez
  15. Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental TrajectoriesAxel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich
  16. Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous DataNazanin Alipourfard, Keith Burghardt and Kristina Lerman
    Section IV: Machine Learning Methods
  17. Machine Learning Methods for Computational Social ScienceRichard D. De Veaux and Adam Eck
  18. Principal Component AnalysisAndreas Pöge and Jost Reinecke
  19. Unsupervised Methods: Clustering MethodsJohann Bacher, Andreas Pöge and Knut Wenzig
  20. Text Mining and Topic ModelingRaphael H. Heiberger and Sebastian Munoz-Najar Galvez
  21. From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content AnalysisGregor Wiedemann and Cornelia Fedtke
  22. Automated Video Analysis for Social Science Research
         Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen

Notă biografică

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.

Descriere

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 scientifi c and engineering sectors.