Big Data and the evaluation of policies

Journal title RIV Rassegna Italiana di Valutazione
Author/s Beba Molinari, Cleto Corposanto
Publishing Year 2018 Issue 2017/68
Language English Pages 19 P. 84-102 File size 515 KB
DOI 10.3280/RIV2017-068006
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In this contribution, the authors aim to demonstrate how Big Data may be a valuable support within a participatory appraisal of the political agenda of a small municipality. The direction for the study was defined by election and inauguration policies which relied heavily on citizen involvement through social media. We will examine how the theoretical approach outlining the framework of evaluative design, in particular mixed-type methods, as well as the various data-mining techniques may be applied to highlight the expressed and unexpressed needs of the electorate. Sentiment analysis, network analysis and web-survey analysis were performed in order understand the level of knowledge and satisfaction with municipal policies, and the results are discussed. We will also show that Big Data is the pivot around which e-governance revolves.

Keywords: Big Data; Evaluation of Policies; Mixed-Methods; Network Analysis; Sentiment Analysis; E-Governance.

  1. Agnoli S., Parra-Saiani P. (2016), Sulle trace dei big data. Questioni di metodo e percorsi di ricerca,
  2. Sociologia e Ricerca Sociale, n.109, FrancoAngeli, Milano.
  3. Arnstein, S.R. (1969), A Ladder of Citizen Participation, “Journal of the American Institute of Planners” 35, pp. 216-24.
  4. Angus D., Rintel S. e Wiles J. (2013), Making sense of big text: a visual-first approach for analyzing text data using Leximancer and Discursis, In International Journal of Scoail Research Methodology, vol. 16, n. 3, pp. 261-267.
  5. Becchi P. (2013), Ciberspazio e democrazia. Come la rete sta cambiando il mondo, in EDemocracy?, a cura di Chiarenza F., Paradoxa, VII, 3, luglio, settembre, 71-83.
  6. de Boeck P. e Wilson M. (2004), Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach. Springer, NY.
  7. Burrows R., Savage M. (2014), After the Crisis? Big Data and the Methodological Challenges of Empirical Sociology, In Big Data and Society, n. 1, pp.1-6, DOI: 10.1177/205395171454028
  8. Chen H., Chiang R., Storey V. (2012), Business Intelligence and Analytics: from big data to big impact, MIS Quarterly Vol. 36 No. 4, pp. 1165-1188.
  9. Chiesi A.M. (1999), L’analisi dei reticoli, FrancoAngeli, Milano.
  10. Corposanto C., Molinari B. (2014), Survey e questionari online, in Corposanto C., Valastro A. (a cura di), Blog, FB & TW, Giuffrè Editore, Milano: 17-42.
  11. Corposanto C., Molinari B. (2015a), Rilevare dati sul web: la cassetta degli attrezzi 2.0 in Sannella A., Toniolo F. (a cura di), Le sfide della società italiana tra crisi strutturali e social innovation, Edizioni Ca’Foscari, Venezia.
  12. Corposanto C., Molinari B. (2015b), Chasing a dragonfly on the lawn, in Science Innovation. Vol. 3, No. 4, 2015, pp. 39-45.
  13. Corposanto C., Molinari B. (2016), Say it with an App, Journal of Advanced Statistics, Vol. 1, No. 2, June 2016, pp. 52-6.
  14. Dalton C, Thatcher J (2014), What does a critical data studies look like, and why do we care? Seven
  15. points for a critical approach to ‘Big Data’. Society and Space open site.
  16. Creswell J.W. Plano Clark V.L. (2011), Designing and Conducting Mixed Methods Research, Sage, Thousand Oaks (CA), 2ND ed.
  17. Della Porta (2010), Democrazia: sfide e opportunità, in Rivista Italiana di Scienza Politica, vol 40, no. 2
  18. Fabris A. (2011), Etica e internet, in Id. (a cura di), Guida alle etiche della comunicazione, ETS, Pisa, pp. 80-106.
  19. Gerrits L. e Verweij S. (2015), “Taking stock of complexity in evaluation: A discussion of three recent publications” in: Evaluation, 21(4), 481-491.
  20. Grimmer J., Stewart B.M. (2013), Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts, In Political Analysis 21(3):267–297
  21. Gross JL, J. Yellen (2004), Handbook of graph theory, CRC Press, London.
  22. Harman, H.H. (1967) Modern Factor Analysis. University Press of Chicago, Chicago. Jurafsky D, James M (2009) Speech and natural language processing: An introduction to natural language processing, computational linguistics, and speech recognition, Upper Saddle River, NJ: Prentice Hall
  23. Kitchin R. (2014), Big Data, New Epistemologies and Paradigm Shifts, In Big Data & Society, I, 1, pp. 1-12, DOI: 10.1177/2053951714528481
  24. Latour B. (1987), Science in Action: How to Follow Scientists and Engineers Through Society, Harvard University Press, Harvard.
  25. Laver M., Benoit K., Garry J. (2003), Extracting policy positions from political texts using words as data, In American Political Science Review, 97(2):311–331
  26. Losito G. (1996), L’analisi del contenuto nella ricerca sociale, FrancoAngeli, Milano.
  27. Marradi A. (1997), Casuale e rappresentativo: ma cosa vuol dire?”, in P. Ceri (a cura di), (1997), Politica e sondaggi, Torino, Rosenberg & Sellier, pp. 23-87, 1997.
  28. Molinari B. (2017), Participation, in Lombi L. Marzulli M., Theorising Sociology in the Digital Society, FrancoAngeli, Milano.
  29. Neresini F. (2015), Quando i numeri diventano grandi: che cosa possiamo imparare dalla scienza, in Rassegna Italiana di Sociologia, 3-4, pp. 405-431.
  30. Pateman C. (1970), Participation and Democratic Theory, Cambridge University Press, Cambridge.
  31. Patton, M.Q. (1994), “Developmental Evaluation”, in: Evaluation Practice, 15(3).
  32. Patton, M.Q. (2011), Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use, Guilford Press, New York-London.
  33. Stame N. (2016), Valutazione pluralista, FrancoAngeli, Milano.
  34. Trobia A. (2014), Web Mining e Application Programming Interfaces per la ricerca sociale: caratteristiche, strumenti, prosepttive e limiti, in Corposanto C. Valastro A., (2014) Blog, FB & TW, Giuffrè Editore, Milano.
  35. Witten I.H., Franck E., Hall M.A. (2011), Data Mining. Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Burlington, MA.

Beba Molinari, Cleto Corposanto, Big Data and the evaluation of policies in "RIV Rassegna Italiana di Valutazione" 68/2017, pp 84-102, DOI: 10.3280/RIV2017-068006