Skip to main content
SpringerLink
Log in
Book cover

International Conference on Decision Support System Technology

ICDSST 2015: Decision Support Systems V – Big Data Analytics for Decision Making pp 34–45Cite as

Making Sense of Governmental Activities Over Social Media: A Data-Driven Approach

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 216))

Abstract

Although social media attracted significant interest from governments throughout the globe, the challenge of a successful exploitation of big social data to gain valuable insights in the decision making process is still unmet. This paper aims to provide policy makers with hints and actionable guidelines for a data-driven analysis of the social accounts they manage. To this aim, we firstly propose a three-dimensional modular framework to structure the analysis; then, the logical steps required within this framework for meaningfully process big social data are detailed by suggesting text mining techniques useful for the analysis. The proposed data-driven approach could lead public administrators to a better understanding of their use of social accounts and to measure the community engagement around some topics of interest. Findings can constitute fresh insights from which public policy makers may draw for enhancing the community involvement and for becoming far more reactive to the citizenry’s needs.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    www.uniteeurope.org, www.padgets.eu, www.sense4us.eu (visited on 02/23/2015).

  2. 2.

    Due to space limits, it has not been possible to enter here code snippets for these text analyses. For further details and to access sample codes the reader is referred, for example, to the public repository http://www.rdatamining.com (visited on 02/23/2015).

References

  1. McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity, Washington DC (2011)

    Google Scholar 

  2. Snijders, C., Matzat, U., Reips, U.D.: “Big Data”: big gaps of knowledge in the field of internet science. Int. J. Internet Sci. 7(1), 1–5 (2012)

    Google Scholar 

  3. Laney, D.: 3-D Data Management: Controlling Data Volume, Velocity and Variety. Gartner Report (2001)

    Google Scholar 

  4. Gerard, G., Haas, M.R., Pentland, A.: Big data and management. Acad. Manag. J. 57(2), 321–326 (2014)

    Article  Google Scholar 

  5. Osella, M.: A Multi-Dimensional Approach for Framing Crowdsourcing Archetypes. Ph.D. thesis (2014). http://porto.polito.it/2535900/. Accessed 23 February 2015

  6. Criado, J.I., Sandoval-Almazan, R., Gil-Garcia, J.R.: Government innovation through social media. Gov. Inf. Q. 30(4), 319–326 (2013)

    Article  Google Scholar 

  7. Bollier, D.: The Promise and Peril of Big Data. The Aspen Institute, Communications and Society Program, Washington (2010)

    Google Scholar 

  8. Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013)

    Article  Google Scholar 

  9. Hemerly, J.: Public Policy Considerations for Data-Driven Innovation. Computer 46(6), 25–31 (2013)

    Article  Google Scholar 

  10. Marsh, J.A., Pane, J.F., Hamilton, L.S.: Making Sense of Data-Driven Decision Making in Education. Rand, Santa Monica (2006)

    Google Scholar 

  11. McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Harvard Bus. Rev. 90(10), 60–66 (2012)

    Google Scholar 

  12. Sackett, D.L.: Evidence-based medicine. Semin. Perinatol. 21, 3–5 (1997)

    Article  Google Scholar 

  13. U.S. Chamber of Commerce Foundation: The future of data-driven innovation (2014)

    Google Scholar 

  14. Brynjolfsson, E., Hitt, L.M., Kim, H.H.: Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? (2011). http://ssrn.com/abstract=1819486. Accessed 23 February 2015

  15. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute (2011)

    Google Scholar 

  16. Spillane, J.P.: Data in practice: conceptualizing the data-based decision-making phenomena. Am. J. Educ. 118(2), 113–141 (2012)

    Article  Google Scholar 

  17. Desouza, K.C., Jacob, B.: Big Data in the Public Sector: Lessons for Practitioners and Scholars. Administration & Society. November 6, 2014. doi:10.1177/0095399714555751

  18. Kim, G.-H., Trimi, S., Chung, J.-H.: Big-data applications in the government sector. Commun. ACM 57(3), 78–85 (2014)

    Article  Google Scholar 

  19. McDonald, P.P.: Managing police operations: Implementing the New York Crime Control Model – CompStat. Wadsworth Publisher, Belmont (2002)

    Google Scholar 

  20. Godown, J.: The CompStat process: Four principles for managing crime reduction. Police Chief. 76(8), 36–42 (2009)

    Google Scholar 

  21. Price-Waterhouse Coopers: Fighting Fraud in the Public Sector. In: Global Economic Crime Survey (2011). https://www.pwc.com/en_GX/gx/psrc/pdf/fighting_fraud_in_the_public_sector_june2011.pdf. Accessed 23 February 2015

  22. Cebr, Centre for Economics and Business Research Ltd. Data equity - Unlocking the value of big data. Report for SAS (2012)

    Google Scholar 

  23. Ferro, E., Loukis, E., Charalabidis, Y., Osella, M.: Policy Making 2.0: from theory to practice. Gov. Inf. Q. 30(4), 359–368 (2013)

    Article  Google Scholar 

  24. OECD: Data-driven Innovation for Growth and Well-being (2014)

    Google Scholar 

  25. Tapscott, D., Williams, A.D., Herman, D.: Government 2.0: Transforming Government and Governance for the Twenty-First Century. New Paradigm, Toronto (2008)

    Google Scholar 

  26. Charalabidis, Y., Gionis, G., Ferro, E., Loukis, E.: Towards a systematic exploitation of web 2.0 and simulation modeling tools in public policy process. In: Tambouris, E., Macintosh, A., Glassey, O. (eds.) ePart 2010. LNCS, vol. 6229, pp. 1–12. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  27. Chun, S.A., Reyes, L., Luis, F.: Social media in government. Gov. Inf. Q. 29(4), 441–445 (2012)

    Article  Google Scholar 

  28. Bertot, J.C., Jaeger, P.T., Munson, S., Glaisyer, T.: Social media technology and government transparency. Computer 43(11), 53–59 (2010)

    Article  Google Scholar 

  29. Snead, J.T.: Social media use in the u.s. executive branch. Gov. Inf. Q. 30(1), 56–63 (2013)

    Article  Google Scholar 

  30. Benčina, J.: Web-based decision support system for the public sector comprising linguistic variables. Informatica 31(3), 311–323 (2007)

    Google Scholar 

  31. Duggan, J.: The case for personal data-driven decision making. In: Proceedings of the 40th International Conference on Very Large Data Bases, 7(11) (2014)

    Google Scholar 

  32. Kadadi, A.: Challenges of data integration and interoperability in big data. In: IEEE International Conference on Big Data, 27–30 October 2014, pp. 38–40 (2014)

    Google Scholar 

  33. Shafer, J., Rixner, S., Cox, A.L.: The hadoop distributed filesystem: balancing portability and performance. In: Proceedings of IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS), pp. 122–133 (2010)

    Google Scholar 

  34. Pandey, S.: Prominence of mapreduce in big data processing. In: Fourth International Conference on Communication Systems and Network Technologies (CSNT), 7–9 April 2014, pp. 555–560. IEEE (2014)

    Google Scholar 

  35. Bolasco, S., Chiari, I., Giuliano, L.: Statistical analysis of textual data. In: Proceedings of the 10th International Conference JADT, LED, Milano, vol. 2, p. 1330 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Istituto Superiore Mario Boella (ISMB), Via P.C. Boggio, 61, 10138, Turin, Italy

    Brunella Caroleo, Andrea Tosatto & Michele Osella

Authors
  1. Brunella Caroleo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Tosatto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Osella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brunella Caroleo .

Editor information

Editors and Affiliations

  1. University of Belgrade, Belgrade, Serbia

    Boris Delibašić

  2. University of Liverpool, Liverpool, United Kingdom

    Jorge E. Hernández

  3. University of Macedonia, Thessaloniki, Greece

    Jason Papathanasiou

  4. SimTech Simulation Technology, Graz, Austria

    Fátima Dargam

  5. Toulouse 1 Capitole University, Toulouse, France

    Pascale Zaraté

  6. UNINOVA – CA3, Lisbon, Portugal

    Rita Ribeiro

  7. University of Plymouth, Plymouth, United Kingdom

    Shaofeng Liu

  8. University of Namur, Namur, Belgium

    Isabelle Linden

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Caroleo, B., Tosatto, A., Osella, M. (2015). Making Sense of Governmental Activities Over Social Media: A Data-Driven Approach. In: Delibašić, B., et al. Decision Support Systems V – Big Data Analytics for Decision Making. ICDSST 2015. Lecture Notes in Business Information Processing, vol 216. Springer, Cham. https://doi.org/10.1007/978-3-319-18533-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18533-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18532-3

  • Online ISBN: 978-3-319-18533-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation