Big data e news online: possibilità e limiti per la ricerca sociale

Titolo Rivista SOCIOLOGIA E RICERCA SOCIALE
Autori/Curatori Giovanni Giuffrida, Francesco Mazzeo Rinaldi, Calogero Zarba
Anno di pubblicazione 2016 Fascicolo 2016/109 Lingua Italiano
Numero pagine 15 P. 159-173 Dimensione file 70 KB
DOI 10.3280/SR2016-109013
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Qui sotto puoi vedere in anteprima la prima pagina di questo articolo.

Se questo articolo ti interessa, lo puoi acquistare (e scaricare in formato pdf) seguendo le facili indicazioni per acquistare il download credit. Acquista Download Credits per scaricare questo Articolo in formato PDF

Anteprima articolo

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

The main aim of this article is to improve knowledge on applicability of Big Data (BD) techniques in social research, by exploring the validity of using BD as an approach in emerging news contexts. In particular, we constructed and examined a large database of historical data of public online comments on a recent constitutional bill review. We using BD technology in order to analyze people’s opinions to this particular reform.;

  1. N. Abbagnano (1961), Dizionario di filosofia, Torino, Utet.
  2. L. Adkins, C. Lury (2012), «Introduction: Special Measures», The Sociological Review, LIX, 2, pp. 5-23, DOI: 10.1111/j.1467-954X.2012.02051.x
  3. M.C. Agodi (2010), «L’estrazione di dati dalla rete: una nota introduttiva», Quaderni di sociologia, LIV, 3, pp. 11-21.
  4. C. Anderson (2008), «The End of Theory: The Data Deluge makes the Scientific Method Obsolete », Wired, June 23, http://www.wired.com/2008/06/pb-theory.
  5. E. Baldacci (2015), «Dal questionario allo smartphone, per la statistica è una rivoluzione», http://www.corrierecomunicazioni.it/it-world/38434_big-data-baldacci-dal-questionarioallo-smartphone-per-la-statistica-e-una-rivoluzione.htm.
  6. E. Baldacci (2016), «From Data to Knowledge: Challenges and Opportunities for Official Statistics », presented at Data Science & Social Research International Conference, Napoli, 17-19 febbraio.
  7. G.C. Bowker (2014), «The Theory/Data Thing», International Journal of Communication, VIII, 5, pp. 1795-9, http://ijoc.org/index.php/ijoc/article/view/2190.
  8. d. boyd, K. Crawford (2012), «Critical Questions for Big Data», Information, Communication & Society, XV, 5, pp. 662-79, DOI: 10.1080/1369118X.2012.678878
  9. L. Clark (2013), «No Questions Asked: Big Data Firm maps Solutions without Human Input », Wired, http://www.wired.co.uk/news/archive/2013-01/16/ayasdi-big-data-launch.
  10. N. Couldry, (2014), «Inaugural: A Necessary Disenchantment: Myth, Agency and Injustice in a Digital World», The Sociological Review, LXII, 4, pp. 880-97, DOI: 10.1111/1467-954X.12158
  11. R. Crompton (2008), «Forty Years of Sociology: Some Comments», Sociology, XLII, 6, pp. 1218-27, DOI: 10.1177/0038038508096942
  12. P. Daboll (2013), «5 Reasons why Big Data will crush Big Research», Forbes, http://www.forbes.com/sites/onmarketing/2013/12/03/5-reasons-why-big-data-will-crush-big-research/.T. Deutsch (2013), «Putting Big Data Myths to rest», http://www.ibmbigdatahub.com/blog/putting-big-data-myths-rest.
  13. J. Dyche (2012), «Big data “Eurekas!” don’t just happen», Harvard Business Review Blog, 20 novembre 2012, http://blog.hbr.org/cs/2012/11/eureka_doesnt_just_happen.html.
  14. P.N. Edwards (2010), A Vast Machine: Computer Models, Climate Data, an the Politics of Global Warming, Cambridge (MA), The Mit Press. L. Einav, J.D. Levin (2013), The Data Revolution and Economic Analysis, Nber Working Paper Series 19035, http://www.nber.org/papers/w19035.
  15. L. Gitelman (2013), «Raw Data» is an Oxymoron (Infrastructures), Cambridge (MA), The Mit Press.
  16. M. Graham (2012), «Big Data and the End of Theory», The Guardian, http://www.guardian.co.uk/news/datablog/2012/mar/09/big-data-theory.
  17. T. Hey, S. Tansley, K. Tolle (eds.) (2009), The Fourth Paradigm: Data-Intensive Scientific Discovery, Redmond (WA), Microsoft Research, http://research.microsoft.com/enus/collaboration/fourthparadigm/.
  18. D. Hume (1748), Philosophical Essays Concerning Human Understanding, London, A. Millar, 1st ed.
  19. D. Karpf (2012), «Social Science Research Methods in internet Time», Information, Communication & Society, XV, 5, pp. 639-61, DOI: 10.1080/1369118X.2012.665468
  20. D. Kiron, P. Kirk Prentice, R. Boucher Ferguson (2014), The Analytics Mandate. Findings from the 2014 Data & Analytics Global Executive Study and Research Report, Mit Sloan Management Review, Research report.
  21. R. Kitchin (2014), «Big Data, New Epistemologies and Paradigm Shifts», Big Data & Society, I, 1, pp. 1-12, DOI: 10.1177/2053951714528481
  22. S. Kumar, F. Morstatter, H. Liu (2013), Twitter Data Analytics, New York (NY), Springer.
  23. C. Lagoze (2014), «Big Data, Data Integrity, and the Fracturing of the Control Zone», Big Data & Society, I, 2, pp. 1-11, DOI: 10.1177/2053951714558281
  24. D. Laney (2001), 3D Data Management: Controlling Data Volume, Variety and Velocity, Technical Report, Meta Group, http://blog.gartner.com/doug-laney/files/2012/01/ad949-
  25. 3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
  26. D. Lazer, R. Kennedy, G. King, A. Vespignani (2014), «The Parable of Google Flu: Traps in Big Data Analysis», Science, CCCXLIII, 6176, pp. 1203-5, DOI: 10.1126/science.1248506
  27. S. Leonelli (2014), «What Difference does Quantity make? On the Epistemology of Big Data in Biology», Big Data & Society, I, 1, pp. 1-11, DOI: 10.1177/2053951714534395
  28. N. Luhmann (2000), Organisation und Entscheidung, Wiesbaden, Opladen Westdeutscher Verlag, tr. it., Organizzazione e decisione, Milano, Mondadori, 2005.
  29. J.G. March (1988), Decisions and Organizations, Oxford, Basil Blackwell; tr. it., Decisioni e organizzazioni, Bologna, il Mulino, 1993.
  30. V. Mayer-Schönberger, K. Cukier (2013), Big Data: A Revolution that will transform how we live, work, and think, New York (NY), Houghton Mifflin Harcourt.
  31. F. Mazzeo Rinaldi, G. Giuffrida, T. Negrete (in press), Real-time Monitoring and Evaluation – Emerging News as Predictive Process using Big Data Based Approach, in G.K. Petersson, F. Leeuw, J.D. Breul (eds.), Big Data and Evaluation, New Brunswick (NJ), Transaction, vol. 24.
  32. D. Murthy, S.A. Bowman (2014), «Big Data Solutions on a Small Scale: Evaluating Accessible High-performance Computing for Social Research», Big Data & Society, I, 2, pp. 1-12, DOI: 10.1177/2053951714559105
  33. K. Pearson (1892), The Grammar of Science, London, Scott; New York, Meridian. Presidenza del Consiglio dei ministri (2014), Constitutional Review Bill, http://www.governo.it/governoinforma/documenti/CONSTITUTIONAL%20REVIEW%20BILL.pdf.
  34. J. Reis, F. Benevenuto, P. Olmo, R. Prates, H. Kwak, J. An (2015), «Breaking the News: First Impressions Matter on Online News», Proceedings of the Ninth International AAAI Conference on web and Social Media, Oxford, Oxford University, pp. 357-66.
  35. A.D. Santana (2011), «Online Readers’ Comments Represent New Opinion Pipeline», Newspaper Research Journal, XXXII, 3, pp. 66-81, DOI: 10.1177/073953291103200306M.Savage,R.Burrows(2007),«TheComingCrisisofEmpiricalSociology»,Sociology,XL,5,pp.885-99,doi:10.1177/0038038507080443
  36. M. Savage, R. Burrows (2009), «Some Further Reflections on the Coming Crisis of Empirical Sociology», Sociology, XLIII, 4, pp. 762-72, DOI: 10.1177/0038038509105420
  37. E. Siegel (2013), Predictive Analytics, Hoboke (NJ), Wiley.
  38. M. Trice (2011), How we Comment on Web Journalism: A Case Study on Dialogue Found in News Articles, in E.D Bruce, K. German (eds.), The Ethics of Emerging Media: Information, Social Norms, and New Media Technology, New York (NY), Continuum.
  39. J. Vitak, P. Zube, A. Smock, C.T. Carr, N. Ellison, C. Lampe (2011), «It’s Complicated: Facebook Users’ Political Participation in the 2008 Election», Cyber-psychology, Behavior, and Social Networking, XIV, 3, pp. 107-14, DOI: 10.1089/cyber.2009.0226
  40. R. Webber (2009), «Response to “The Coming Crisis of Empirical Sociology”: An Outline of the Research Potential of Administrative and Transactional Data», Sociology, XLIII, 1, pp. 169-78, DOI: 10.1177/0038038508099104
  41. R. Zafarani, M.A. Abbasi, H. Liu (2014), Social Media Mining. An Introduction, Cambridge, Cambridge University Press.
  42. R. Zamith, S.C. Lewis (2014), «From Public Spaces to Public Sphere: Rethinking Systems for Reader Comments on Online News Sites», Digital Journalism, II, 4, pp. 558-74, DOI: 10.1080/21670811.2014.882066

  • Building Decision-making Indicators Through Network Analysis of Big Data Venera Tomaselli, Giovanni Giuffrida, Simona Gozzo, Francesco Mazzeo Rinaldi, in Social Indicators Research /2020 pp.33
    DOI: 10.1007/s11205-020-02363-2
  • Handbook of Research on Advanced Research Methodologies for a Digital Society Felice Addeo, Valentina D'Auria, pp.24 (ISBN:9781799884736)
  • Handbook of Research on Advanced Research Methodologies for a Digital Society Costantino Cipolla, pp.42 (ISBN:9781799884736)
  • The Phases of Qualitative and Quantitative Methods in Italian Sociology: Institutionalisation, Social Engagement, and Emerging Problems Giuseppe Masullo, in The American Sociologist /2024
    DOI: 10.1007/s12108-024-09615-5
  • Big Data e Valutazione: una relazione ancora da costruire Francesco Mazzeo Rinaldi, in RIV Rassegna Italiana di Valutazione 68/2018 pp.7
    DOI: 10.3280/RIV2017-068002

Giovanni Giuffrida, Francesco Mazzeo Rinaldi, Calogero Zarba, Big data e news online: possibilità e limiti per la ricerca sociale in "SOCIOLOGIA E RICERCA SOCIALE " 109/2016, pp 159-173, DOI: 10.3280/SR2016-109013