LEADER 04165nam 22006495 450 001 996546840203316 005 20230124114014.0 010 $a3-031-16624-8 024 7 $a10.1007/978-3-031-16624-2 035 $a(CKB)5710000000108207 035 $a(MiAaPQ)EBC7186257 035 $a(Au-PeEL)EBL7186257 035 $a(OCoLC)1368010371 035 $a(DE-He213)978-3-031-16624-2 035 $a(PPN)267811578 035 $a(EXLCZ)995710000000108207 100 $a20230123d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHandbook of Computational Social Science for Policy$b[electronic resource] /$fedited by Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (497 pages) 311 $a3-031-16623-X 330 $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 problems in 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 data that can be used to study social sciences and are interested in achieving a policy impact. 606 $aArtificial intelligence?Data processing 606 $aQuantitative research 606 $aSociology?Methodology 606 $aMachine learning 606 $aData Science 606 $aData Analysis and Big Data 606 $aSociological Methods 606 $aMachine Learning 615 0$aArtificial intelligence?Data processing. 615 0$aQuantitative research. 615 0$aSociology?Methodology. 615 0$aMachine learning. 615 14$aData Science. 615 24$aData Analysis and Big Data. 615 24$aSociological Methods. 615 24$aMachine Learning. 676 $a005.7 700 $aBertoni$b Eleonora$01338292 701 $aFontana$b Matteo$01338293 701 $aGabrielli$b Lorenzo$01338294 701 $aSignorelli$b Serena$01338295 701 $aVespe$b Michele$01338296 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546840203316 996 $aHandbook of Computational Social Science for Policy$93058147 997 $aUNISA