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oapen-20.500.12657-514122021-11-12T02:47:42Z Chapter 7 Digital Trace Data Keusch, Florian Kreuter, Frauke AI, big data, data analysis, data archives, data ownership, data science, digital trace, ethical standards, ethics, human-robot interaction, information technology, machine learning, open data, politics, policy, quantitative, replication, social, social media, socio-robots, survey data, survey design, survey methodology, unstructured data bic Book Industry Communication::J Society & social sciences::JM Psychology bic Book Industry Communication::J Society & social sciences::JM Psychology::JMB Psychological methodology "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 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." 2021-11-11T11:03:38Z 2021-11-11T11:03:38Z 2021 chapter 9780367456535 9780367456528 https://library.oapen.org/handle/20.500.12657/51412 eng application/pdf Attribution-NonCommercial-NoDerivatives 4.0 International 9781003024583_10.4324_9781003024583-8.pdf Taylor & Francis Handbook of Computational Social Science, Vol 1 Routledge 10.4324/9781003024583-8 10.4324/9781003024583-8 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb 3b8c3127-84ee-411d-803a-d657dc2cad84 9780367456535 9780367456528 Routledge 20 open access
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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 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."
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