Big Data Meet Pharmaceutical Industry: An Application on Social Media Data

  • Caterina LiberatiEmail author
  • Paolo Mariani
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Big Data are hard to capture, store, search, share, analyze, and visualize. Without any doubts, Big Data represent the new frontier of data analysis, although their manipulation is far to be realized by standard computing machines. In this paper, we present a strategy to process and extract knowledge from Facebook data, in order to address marketing actions of a pharmaceutical company. The case study relies on a large Italians sample, interested in wellness and health care. The results of the study are very sturdy and can be easily replicated in different contexts.


Big Data Dimensions reduction Knowledge extraction Healthcare sector 


  1. 1.
    Boyd, D., Crawford, K.: Critical questions for Big Data. Inf. Commun. Soc. 15, 662–679 (2012)CrossRefGoogle Scholar
  2. 2.
    Douglas, L.: 3D Data Management: Controlling Data Volume, Velocity and Variety. Technical report, META Group. (2001)
  3. 3.
    McAfee, A., Brynjolfsson, E.: Big Data: the management revolution. Harvard Bus. Rev. 10, 59–68 (2012)Google Scholar
  4. 4.
    Demchenko, Y., Grosso, P., de Laat, C., Membrey, P.: Addressing Big Data issues in Scientific Data Infrastructure. Proceedings of 2013 International Conference on Collaboration Technologies and Systems (CTS), IEEE, pp. 48–55 (2013)Google Scholar
  5. 5.
    McNulty-Holmes, E.: Understating Big Data: the seven V’s. (2014)
  6. 6.
    The Economist: New rules for Big Data. (2010)
  7. 7.
    Falotico, R., Liberati, C., Zappa, P.: Identifying oncological patient information needs to improve e-health communication: a preliminary text-mining analysis. Qual. Reliab. Engng. Int. 31, 1115–1126 (2015)CrossRefGoogle Scholar
  8. 8.
    The Economist: The data deluge. Businesses, governments and society are only starting to tap its vast potential. (2010)
  9. 9.
    Lazer, D., Kennedy, R.: What We Can Learn From the Epic Failure of Google Flu Trends. Wired. (2015)
  10. 10.
    Santoro, E.: Web 2.0 e Medicina: come social network, podcast, wiki e blog trasformano la comunicazione, l’assistenza e la formazione in sanitá. Il pensiero scientifico Editore, Milano (2009)Google Scholar
  11. 11.
    Cubeyou: How pharmaceutical market customers benchmark against average Italian people, Industry Report, pp. 1–36 (2014)Google Scholar
  12. 12.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behaviour. Proc. US Natl. Acad. Sci. 110(15), 5802–5805 (2013)CrossRefGoogle Scholar
  13. 13.
    Bolasco, S.: Analisi Multidimensional dei dati. Carocci, Roma (2010)Google Scholar
  14. 14.
    Moubarak, G., Guiot, A., Benhamou, Y., Hariri, S.: Relationship and its impact on the doctor-patient Facebook activity of residents and fellows. J. Med. Ethics. 37, 101–104 (2010)CrossRefGoogle Scholar
  15. 15.
    Greene, J.A., Kesselheim, A.S.: Pharmaceutical marketing and the new social media. N. Engl. J. Med. 363, 2087–2089 (2010)CrossRefGoogle Scholar
  16. 16.
    Mariani, P., Mussini, M.: L’integrazione di dati amministrativi e campionari per arricchire i sistemi informativi di marketing nel farmaceutico: evidenze empiriche legate al record linkage. Micro Macro Mark. 2, 295–318 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.DEMSUniversitá degli Studi di Milano-BicoccaMilanItaly

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