Clicca qui per scaricare

Big data: un’opportunità per le scienze sociali?
Titolo Rivista: SOCIOLOGIA E RICERCA SOCIALE  
Autori/Curatori: Rosaria Conte 
Anno di pubblicazione:  2016 Fascicolo: 109 Lingua: Italiano 
Numero pagine:  10 P. 18-27 Dimensione file:  48 KB
DOI:  10.3280/SR2016-109003
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più:  clicca qui   qui 


In recent years, Big Data and Big Data science are frontstage both in prestigious scientific institutions around the world, and in the media. Italy has contributed to the launch of Big Data science in Europe. With more than eight hundred million quotes on Google, Big Data is one of the most cited technoscientific tags. Furthermore, since 2009, when Lazar and colleagues published the famous «Computational Social Science» on Science, Big Data science gave impulse to a new formidable endeavor: the creation of a new quantitative social science through the extensive application of Big Data science to the study of social phenomena. Now, a few years after the launch of the quantitative science of society, it is time for a first evaluation. Did Big Data maintain its promises? Does it still represent an opportunity for a new science of society? The answer provided in this paper is mildly positive. Big Data represents a good opportunity for the innovation of the social sciences on condition that (a) the application of data science to social data is global, rather than local, and oriented to policy, rather than profit-making; (b) the objective of predicting future events does not inhibit the complementary objective of science, i.e. explanatory speculation; and finally (c) quantitative science does not lead to dispense away with the understanding of the cognitive, social, cultural, and political mechanisms that generate social data


  1. B. Arthur (2006), Out-of-Equilibrium Economics and Agent-Based Modeling, in L. Tesfatsion, K.L. Judd (eds.), Handbook of Computational Economics, vol. 2, Amsterdam, Elsevier.
  2. A. Bessi, M. Coletto, G.A. Davidescu, A. Scala, G. Caldarelli, W. Quattrociocchi (2015), «Science vs Conspiracy: Collective Narratives in the Age of Misinformation», PLoS One, X, 2: e0118093,, DOI: 10.1371/journal.pone.0118093
  3. R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, J. Kertesz, V. Loreto, S.
  4. Moat, J.-P. Nadal, A. Sanchez, A. Nowak, A. Flache, M. San Miguel, D. Helbing (2012), «Manifesto of Computational Social Science», The European Physical Journal Special Topics, 214, pp. 325-46,, DOI: 10.1140/epjst/e2012-01697-8
  5. R. Conte, F. Giardini (2015), «Towards Computational and Behavioral Social Science», European Psychologist, forthcoming.
  6. S. Davison (2015), «An Exploratory Study of Risk and Social Media: What Role did Social Media play in the Arab Spring Revolutions?», Journal of Middle East Media, 11, ttp://jmem.gsu.edu/files/2014/07/JMEM-2015_ENG_Davison.pdf.
  7. J.M. Epstein (2006), Generative Social Science: Studies in Agent-Based Computational Modeling, Princeton, Princeton University Press.
  8. F. Giannotti, D. Pedreschi, A. Pentland, P. Lukowicz, D. Kossmann, J. Crowley, D. Helbing (2012), «A Planetary Nervous System for Social Mining and Collective Awareness», The European Physical Journal Special Topics, 214, pp. 49-75,, DOI: 10.1140/epjst/e2012-01688-9
  9. G.J. Gumerman (ed.) (1988), The Anasazi in a Changing Environment, Cambridge, Cambridge University Press.
  10. D. Hume (1739-1740), Treatise of Human Nature, in D.F. Norton & M.J. Norton (eds.), Oxford Philosophical Texts, Oxford, Oxford University Press, 2000.
  11. D. Lazer, A. Pentland, L. Adamic, S. Aral, A.L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, M. Van Alstyne (2009), «Life in the Network: The Coming Age of Computational Social Science», Science, CCCXXIII, 5915, pp. 721-3,, DOI: 10.1126/science.1167742
  12. R. Pastor-Satorras, C. Castellano, P. Van Mieghem, A. Vespignani (2015), «Epidemic Processes in Complex Networks», Reviews of Modern Physics, 87, pp. 925-79,, DOI: 10.1103/RevModPhys.87.925
  13. T. Schelling (1971), «Dynamic Models of Segregation», Journal of Mathematical Sociology, I, 2, pp. 143-86.
  14. C. Scherer, H. Cho (2003), «A Social Contagion Theory of Risk Perception», Risk Analysis, XXIII, 2, pp. 261-7.



  1. Biagio Aragona, Types of Big Data and designs of evaluation research in RIV Rassegna Italiana di Valutazione 68/2018 pp. 48, DOI: 10.3280/RIV2017-068004

Rosaria Conte, in "SOCIOLOGIA E RICERCA SOCIALE " 109/2016, pp. 18-27, DOI:10.3280/SR2016-109003

   

FrancoAngeli è membro della Publishers International Linking Association associazione indipendente e no profit per facilitare l'accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche