Handbook of Computational Social Science, Volume 1

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Agent Based Modeling
agent-based simulation
AI
analytical sociology
big data
Category=GPS
Category=JHBC
Category=JMBT
Category=UBJ
Common Language
Comparative Risk Perception
Computational Cognitive Modeling
computational modelling
CSS
data analysis
Data Archives
data ownership
data science
digital behaviour research
digital trace
Digital Trace Data
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
ethical issues in computational social research
ethical standards
ethics
Highest Suitability Scores
Human Robot Interaction
information technology
ISIS Ideology
ISIS's Propaganda
ISIS’s Propaganda
LDA
LIWC Category
machine learning
open data
politics
Principled Ai
privacy
privacy in data science
Public Engagement
quantitative
replication
Research Data Archiving
Rest API
Robot Characteristics
social
social media
Social Media Data
Social Media Datasets
social network analysis
Social Robots
Social Science Research
Social Simulation
socio-robotics
survey data
survey design
survey methodology
UK Census Data
unstructured data
Vice Versa

Product details

  • ISBN 9780367456535
  • Weight: 1040g
  • Dimensions: 174 x 246mm
  • Publication Date: 17 Nov 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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 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 in 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.

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 e- ects of social media and AI, social media and well-being, and how the design of social robots impacts 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.