Handbook of Computational Social Science - Vol 1 & Vol 2
1st Edition
Published on 17. November 2021
Book
848 pages
978-1-032-11139-1 (ISBN)
Description
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. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.
The 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.
The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions.
The 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.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Dimensions
Height: 246 mm
Width: 174 mm
Weight
680 gr
ISBN-13
978-1-032-11139-1 (9781032111391)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Uwe Engel | Anabel Quan-Haase | Sunny Xun Liu
Handbook of Computational Social Science - Vol 1 & Vol 2
Book
11/2021
1st Edition
Routledge
€178.54
Shipment within 10-20 days
Persons
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.
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.
Content
Volume 1
Preface
Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. The Scope and Boundaries of CSS
The Scope of Computational Social Science
Claudio Cioffi-Revilla
Analytical Sociology amidst a Computational Social Science Revolution
Benjamin F. Jarvis, Marc Keuschnigg and Peter Hedstroem
Computational Cognitive Modeling in the Social Sciences
Holger Schultheis
Computational Communication Science: Lessons from Working Group Sessions with Experts of an Emerging Research Field
Stephanie Geise and Annie Waldherr
A Changing Survey Landscape
Lars Lyberg and Steven G. Heeringa
Digital Trace Data: Modes of Data Collection, Applications, and Errors at a Glance
Florian Keusch and Frauke Kreuter
Open Computational Social Science
Jan G. Voelkel and Jeremy Freese
Causal and Predictive Modeling in Computational Social Science
Uwe Engel
Data-driven Agent-based Modeling in Computational Social Science
Jan Lorenz
Section II. Privacy, Ethics, and Politics in CSS Research
Ethics and Privacy in Computational Social Science: A Call for Pedagogy
William Hollingshead, Anabel Quan-Haase and Wenhong Chen
Deliberating with the Public: An Agenda to Include Stakeholder Input on Municipal "Big Data" Projects
James Popham, Jennifer Lavoie, Andrea Corradi and Nicole Coomber
Analysis of the Principled-AI Framework?s Constraints in Becoming a Methodological Reference for Trustworthy-AI Design
Daniel Varona and Juan Luis Suarez
Section III. Case Studies and Research Examples
Sensing Close-Range Proximity for Studying Face-to-Face Interaction
Johann Schaible, Marcos Oliveira, Maria Zens and Mathieu Genois
Social Media Data in Affective Science
Max Pellert, Simon Schweighofer and David Garcia
Understanding Political Sentiment: Using Twitter to Map the US 2016 Democratic Primaries
Niklas M Loynes and Mark J Elliot
The Social Influence of Bots and Trolls in Social Media
Yimin Chen
Social Bots and Social Media Manipulation in 2020: The Year in Review
Ho-Chun Herbert Chang, Emily Chen, Meiqing Zhang, Goran Muric, and Emilio Ferrara
A Picture is (still) Worth a Thousand Words: The Impact of Appearance and Characteristic Narratives on People's Perceptions of Social Robots
Sunny Xun Liu, Elizabeth Arredondo, Hannah Miezkowski, Jeff Hancock and Byron Reeves
Data Quality and Privacy Concerns in Digital Trace Data: Insights from a Delphi Study on Machine Learning and Robots in Human Life
Uwe Engel and Lena Dahlhaus
Effective Fight Against Extremist Discourse On-Line: The Case of ISIS's Propaganda
Seraphin Alava and Rasha Nagem
Public Opinion Formation on the Far Right
Michael Adelmund and Uwe Engel
Volume 2
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 Juenger
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 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 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 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
Preface
Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. The Scope and Boundaries of CSS
The Scope of Computational Social Science
Claudio Cioffi-Revilla
Analytical Sociology amidst a Computational Social Science Revolution
Benjamin F. Jarvis, Marc Keuschnigg and Peter Hedstroem
Computational Cognitive Modeling in the Social Sciences
Holger Schultheis
Computational Communication Science: Lessons from Working Group Sessions with Experts of an Emerging Research Field
Stephanie Geise and Annie Waldherr
A Changing Survey Landscape
Lars Lyberg and Steven G. Heeringa
Digital Trace Data: Modes of Data Collection, Applications, and Errors at a Glance
Florian Keusch and Frauke Kreuter
Open Computational Social Science
Jan G. Voelkel and Jeremy Freese
Causal and Predictive Modeling in Computational Social Science
Uwe Engel
Data-driven Agent-based Modeling in Computational Social Science
Jan Lorenz
Section II. Privacy, Ethics, and Politics in CSS Research
Ethics and Privacy in Computational Social Science: A Call for Pedagogy
William Hollingshead, Anabel Quan-Haase and Wenhong Chen
Deliberating with the Public: An Agenda to Include Stakeholder Input on Municipal "Big Data" Projects
James Popham, Jennifer Lavoie, Andrea Corradi and Nicole Coomber
Analysis of the Principled-AI Framework?s Constraints in Becoming a Methodological Reference for Trustworthy-AI Design
Daniel Varona and Juan Luis Suarez
Section III. Case Studies and Research Examples
Sensing Close-Range Proximity for Studying Face-to-Face Interaction
Johann Schaible, Marcos Oliveira, Maria Zens and Mathieu Genois
Social Media Data in Affective Science
Max Pellert, Simon Schweighofer and David Garcia
Understanding Political Sentiment: Using Twitter to Map the US 2016 Democratic Primaries
Niklas M Loynes and Mark J Elliot
The Social Influence of Bots and Trolls in Social Media
Yimin Chen
Social Bots and Social Media Manipulation in 2020: The Year in Review
Ho-Chun Herbert Chang, Emily Chen, Meiqing Zhang, Goran Muric, and Emilio Ferrara
A Picture is (still) Worth a Thousand Words: The Impact of Appearance and Characteristic Narratives on People's Perceptions of Social Robots
Sunny Xun Liu, Elizabeth Arredondo, Hannah Miezkowski, Jeff Hancock and Byron Reeves
Data Quality and Privacy Concerns in Digital Trace Data: Insights from a Delphi Study on Machine Learning and Robots in Human Life
Uwe Engel and Lena Dahlhaus
Effective Fight Against Extremist Discourse On-Line: The Case of ISIS's Propaganda
Seraphin Alava and Rasha Nagem
Public Opinion Formation on the Far Right
Michael Adelmund and Uwe Engel
Volume 2
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 Juenger
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 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 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 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