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dc.contributor.authorJarvis, Benjamin
dc.contributor.authorKeuschnigg, Marc
dc.contributor.authorHedström, Peter
dc.date.accessioned2021-11-12T04:07:23Z
dc.date.available2021-11-12T04:07:23Z
dc.date.issued2021
dc.date.submitted2021-11-11T10:56:07Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/51411
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/72766
dc.description.abstract"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."
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::J Society & social sciences::JM Psychology
dc.subject.classificationbic Book Industry Communication::J Society & social sciences::JM Psychology::JMB Psychological methodology
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JM Psychologyen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodologyen_US
dc.subject.otherAI, 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
dc.titleChapter 3 Analytical Sociology amidst a Computational Social Science Revolution
dc.typechapter
oapen.identifier.doi10.4324/9781003024583-4
oapen.relation.isPublishedByfa69b019-f4ee-4979-8d42-c6b6c476b5f0
oapen.relation.isPartOfBookdf549f31-e9f4-4c61-b406-97935620d6b3
oapen.relation.isbn9780367456535
oapen.relation.isbn9780367456528
oapen.imprintRoutledge
oapen.pages21
oapen.review.commentsTaylor & Francis open access titles are reviewed as a minimum at proposal stage by at least two external peer reviewers and an internal editor (additional reviews may be sought and additional content reviewed as required).
oapen.peerreviewProposal review
peerreview.review.typeProposal
peerreview.anonymitySingle-anonymised
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityPublisher
peerreview.idbc80075c-96cc-4740-a9f3-a234bc2598f1
peerreview.titleProposal review


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