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001 978-3-031-16624-2
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020 _a9783031166242
_9978-3-031-16624-2
024 7 _a10.1007/978-3-031-16624-2
_2doi
050 4 _aQ336
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
245 1 0 _aHandbook of Computational Social Science for Policy
_h[electronic resource] /
_cedited by Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXXI, 490 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
506 0 _aOpen Access
520 _aThis open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problemsin the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding datathat can be used to study social sciences and are interested in achieving a policy impact.
650 0 _aArtificial intelligence
_xData processing.
650 0 _aQuantitative research.
650 0 _aSociology
_xMethodology.
650 0 _aMachine learning.
650 1 4 _aData Science.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aSociological Methods.
650 2 4 _aMachine Learning.
700 1 _aBertoni, Eleonora.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aFontana, Matteo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGabrielli, Lorenzo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSignorelli, Serena.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aVespe, Michele.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031166235
776 0 8 _iPrinted edition:
_z9783031166259
776 0 8 _iPrinted edition:
_z9783031166266
856 4 0 _uhttps://doi.org/10.1007/978-3-031-16624-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
942 _cSPRINGER
999 _c176628
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