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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
www.uniteeurope.org, www.padgets.eu, www.sense4us.eu (visited on 02/23/2015).
- 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
McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity, Washington DC (2011)
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)
Laney, D.: 3-D Data Management: Controlling Data Volume, Velocity and Variety. Gartner Report (2001)
Gerard, G., Haas, M.R., Pentland, A.: Big data and management. Acad. Manag. J. 57(2), 321–326 (2014)
Osella, M.: A Multi-Dimensional Approach for Framing Crowdsourcing Archetypes. Ph.D. thesis (2014). http://porto.polito.it/2535900/. Accessed 23 February 2015
Criado, J.I., Sandoval-Almazan, R., Gil-Garcia, J.R.: Government innovation through social media. Gov. Inf. Q. 30(4), 319–326 (2013)
Bollier, D.: The Promise and Peril of Big Data. The Aspen Institute, Communications and Society Program, Washington (2010)
Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013)
Hemerly, J.: Public Policy Considerations for Data-Driven Innovation. Computer 46(6), 25–31 (2013)
Marsh, J.A., Pane, J.F., Hamilton, L.S.: Making Sense of Data-Driven Decision Making in Education. Rand, Santa Monica (2006)
McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Harvard Bus. Rev. 90(10), 60–66 (2012)
Sackett, D.L.: Evidence-based medicine. Semin. Perinatol. 21, 3–5 (1997)
U.S. Chamber of Commerce Foundation: The future of data-driven innovation (2014)
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
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)
Spillane, J.P.: Data in practice: conceptualizing the data-based decision-making phenomena. Am. J. Educ. 118(2), 113–141 (2012)
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
Kim, G.-H., Trimi, S., Chung, J.-H.: Big-data applications in the government sector. Commun. ACM 57(3), 78–85 (2014)
McDonald, P.P.: Managing police operations: Implementing the New York Crime Control Model – CompStat. Wadsworth Publisher, Belmont (2002)
Godown, J.: The CompStat process: Four principles for managing crime reduction. Police Chief. 76(8), 36–42 (2009)
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
Cebr, Centre for Economics and Business Research Ltd. Data equity - Unlocking the value of big data. Report for SAS (2012)
Ferro, E., Loukis, E., Charalabidis, Y., Osella, M.: Policy Making 2.0: from theory to practice. Gov. Inf. Q. 30(4), 359–368 (2013)
OECD: Data-driven Innovation for Growth and Well-being (2014)
Tapscott, D., Williams, A.D., Herman, D.: Government 2.0: Transforming Government and Governance for the Twenty-First Century. New Paradigm, Toronto (2008)
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)
Chun, S.A., Reyes, L., Luis, F.: Social media in government. Gov. Inf. Q. 29(4), 441–445 (2012)
Bertot, J.C., Jaeger, P.T., Munson, S., Glaisyer, T.: Social media technology and government transparency. Computer 43(11), 53–59 (2010)
Snead, J.T.: Social media use in the u.s. executive branch. Gov. Inf. Q. 30(1), 56–63 (2013)
Benčina, J.: Web-based decision support system for the public sector comprising linguistic variables. Informatica 31(3), 311–323 (2007)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)