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Building Decision-making Indicators Through Network Analysis of Big Data

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Abstract

In recent years we have witnessed a growing concern in scientific research to understand and improve actionable analytics-driven decision processes, mostly focused on online data. Many researchers have focused their attention on computational and Information and Communications Technology issues in this matter. Only a small share of literature is concerned with how indicators can be improved by Big Data analytics. In this paper, we propose an innovative methodological approach to building indicators by combining Big Data analytics with the analysis of network patterns. Our study aims to define relational structures in a Big Data set, implementing measurements and clustering methods by Network Analysis in order to build decision-making indicators. We describe an audience model both to collect a large amount of online data from large online newspapers and to structure those in a relational form. By analysing readers’ comments, we can derive proxies of reliable indicators about specific topics discussed on an online newspaper blog. We show the effectiveness of such an approach in detecting and building indicators to support policy-makers in complex decision-making processes.

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Notes

  1. Dandelion is a commercial package based on the TagMe research project lead by Professor Ferragina of University of Pisa (Ferragina and Scaiella 2010). TagMe uses an interesting algorithms to link 'concepts' in the given text with proper Wikipedia pages. This then gives access to the Dbpedia knowledge base, which is very vast.

References

  • Agnoli, S., & Parra Saiani, P. (Eds.) (2016). Sulle tracce dei Big Data. Questioni di metodo e percorsi di ricerca. Sociologia e Ricerca Sociale, 109, https://doi.org/10.3280/sr2016-109013.

  • Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Review of Modern Physics,74(1), 47–97.

    Article  Google Scholar 

  • Asero, V., Gozzo, S., & Tomaselli, V. (2016). Building tourism networks through tourist mobility. Journal of Travel Research,55(6), 751–763. https://doi.org/10.1177/0047287515569777.

    Article  Google Scholar 

  • Bamberger, M. (2016). Integrating Big Data into the monitoring and evaluation of development programmes. New York: UN Global Pulse.

    Google Scholar 

  • Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks,19(3), 243–269.

    Article  Google Scholar 

  • Cronin, B. (2011). A window on emergent European social network analysis. Procedia Social and Behavioral Sciences,10, 1–4. https://doi.org/10.1016/j.sbspro.2011.01.001.

    Article  Google Scholar 

  • Cross, R., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Boston: Harvard Business School Press.

    Google Scholar 

  • Daboll, P. (2013). 5 Reasons Why Big Data Will Crush Big Research. Available at: http://www.forbes.com/sites/onmarketing/2013/12/03/5-reasons-why-big-data-will-crush-big-research.

  • Davis, R. (2009). The use of S.N.A. tools in the evaluation of social change communications. Available at: http://mande.co.uk/blog/wp-content/uploads/2009/10/The-Use-of-Social-Network-Analysis-Tools-in-the-Evaluation-of-Social-Change-Communications-C.pdf.

  • de Nooy, W. (2015). Structure from interactive events. Big Data & Society,10, 1–4. https://doi.org/10.1177/2053951715603732.

    Article  Google Scholar 

  • Dhruva, M. S., & Shashikala, M. K. (2017). Survey on Big Data analytics. International Journal of Engineering and Applied Computer Science,2(6), 181–185.

    Article  Google Scholar 

  • Di Bella, E., Leporatti, L., & Maggino, F. (2018). Big data and social indicators: actual trends and new perspectives. Social Indicators Research,135(3), 869–878. https://doi.org/10.1007/s11205-016-1495-y.

    Article  Google Scholar 

  • Doreian, P., Lloyd, P., & Mrvar, A. (2013). Partitioning large signed two-mode networks: Problems and prospects. Social Networks,35(2), 178–203.

    Article  Google Scholar 

  • Ferragina, P., & Scaiella, U. (2010). TAGME: On-the-fly Annotation of Short Text Fragments (by Wikipedia Entities). CIKM’10, October 25–29, 2010, Toronto, Ontario, Canada.

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management,35(2), 137–144.

    Article  Google Scholar 

  • Gartner. (2012). ‘Big data’. http://www.gartner.com/it-glossary/bigdata/. Accessed 9 July, 2013.

  • Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics,55(4), 665–676.

    Article  Google Scholar 

  • Giuffrida, G., Mazzeo Rinaldi, F., & Zarba, C. (2016). Big data e news online: possibilità e limiti per la ricerca sociale. In S. Agnoli, & P. Parra Saiani (Eds.), Sulle Tracce dei Big Data. Questioni di Metodo e Percorsi di Ricerca. Sociologia e Ricerca Sociale, 109, 159–173.

  • Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology,40(1), 129–152.

    Article  Google Scholar 

  • Gonzàlez-Bailón, S. (2013). Social science in the era of big data. Policy and Internet,5(2), 147–160.

    Article  Google Scholar 

  • Gozzo, S., & Tomaselli, V. (2016). Network analysis methods to measure sociometric status. Mathematics and Statistics,4(3), 81–94. https://doi.org/10.13189/ms.2016.040303.

    Article  Google Scholar 

  • Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying twitter topic-networks using social-network analysis. Social Media +Society. https://doi.org/10.1177/2056305117691545.

    Article  Google Scholar 

  • Hu, J., & Zhang, Y. (2017). Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization. Scientometrics,112, 91–109.

    Article  Google Scholar 

  • Jussila, J., Menon, K., Gupta, J., & Kärkkäinen, H. (2017). Who is who in Big Social Data? A Bibliographic Network Analysis Study. In Proceedings of the 4th European conference on social media ECSM 2017 (Vol. 4, pp. 161–169). Reading: Academic Conferences and Publishing International Limited.

  • Kettl, D. F. (2017). Little bites of Big Data for public policy. London: Sage.

    Google Scholar 

  • Kretschmer, H., & Aguillo, I. (2004). Visibility of collaboration on the web. Scientometrics,61(3), 405–426.

    Article  Google Scholar 

  • Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note,6, 70.

    Google Scholar 

  • Laumann, E. O., & Pappi, F. U. (1976). Networks of collective action: A perspective on community influence systems. New York: Academic Press.

    Google Scholar 

  • Marchetti, S., Giusti, C., Pratesi, M., Salvati, N., Giannotti, F., Pedreschi, D., et al. (2015). Small area model-based estimators using big data sources. Journal of Official Statistics,31(2), 263–281.

    Article  Google Scholar 

  • Mazzeo Rinaldi, F., Giuffrida, G., & Negrete, T. (2017). Real-time monitoring and evaluation. Emerging news as predictive process using big data based approach. In G. J. Petersson & J. D. Breul (Eds.), Cyber society, big data and evaluation (pp. 191–214). New Brunswick, NJ: Transaction Publishers.

    Chapter  Google Scholar 

  • McFarland, D. A., & McFarland, H. R. (2015). Big data and the danger of being precisely inaccurate. Big Data & Society. https://doi.org/10.1177/2053951715602495.

    Article  Google Scholar 

  • Otte, E., & Rousseau, R. (2002). Social network analysis a powerful strategy, also for the information sciences. Journal of Information Science,28(6), 441–453.

    Article  Google Scholar 

  • Peng, Y., Shi, J., Fantinato, M., & Chen, J. (2017). A study on the author collaboration network in big data. Information Systems Frontiers,19, 1329–1342.

    Article  Google Scholar 

  • Petersson, G. J., & Breul, J. D. (Eds.). (2017). Cyber society, big data and evaluation. New Brunswick, NJ: Transaction Publishers.

    Google Scholar 

  • Prell, C. (2012). Social network analysis: History, theory & methodology. London: Sage Publications Inc.

    Google Scholar 

  • Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Reader, S. M., & Leris, I. (2014). What shapes social decision making? (Open peer commentary). Behavioral and Brain Sciences,37(1), 96–97.

    Article  Google Scholar 

  • Reis, J., Benevenuto, F., Olmo, P., Prates, R., Kwak, H., & An, J. (2015). Breaking the news: First impressions matter on online news. In Proceedings of the ninth international AAAI conference on web and social media (pp. 357–366), Oxford: Oxford University.

  • Savitz, E. (2012). Gartner: 10 Critical tech trends for the next five years. Forbes. Retrieved from http://www.forbes.com/sites/ericsavitz/2012/10/22/gartner-10-critical-tech-trends-for-the-nextfive-years.

  • Sudhahar, S., Veltri, G. A., & Cristianini, N. (2015). Automated analysis of the US presidential elections using big data and network analysis. Big Data & Society. https://doi.org/10.1177/2053951715572916.

    Article  Google Scholar 

  • Taylor, L., Schroeder, R., & Meyer, E. (2014). Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same? Big Data & Society,1(2), 1–10.

    Google Scholar 

  • Wasserman, S., & Faust, K. (1997). Social network analysis: Methods and application. New York: Cambridge University Press.

    Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature,393(4), 440–442.

    Article  Google Scholar 

  • Yan, E., Ding, Y., & Zhu, Q. (2010). Mapping library and information science in China: A coauthorship network analysis. Scientometrics,83(1), 115–131.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Political and Social Sciences, University of Catania 8, Vittorio Emanuele II, 95131, Catania, Italy

    Venera Tomaselli, Giovanni Giuffrida, Simona Gozzo & Francesco Mazzeo Rinaldi

  2. School of Architecture and the Built Environment, KTH, Royal Institute of Technology, Stockholm, Sweden

    Francesco Mazzeo Rinaldi

Authors
  1. Venera Tomaselli
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  2. Giovanni Giuffrida
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  3. Simona Gozzo
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  4. Francesco Mazzeo Rinaldi
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Correspondence to Venera Tomaselli.

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Tomaselli, V., Giuffrida, G., Gozzo, S. et al. Building Decision-making Indicators Through Network Analysis of Big Data. Soc Indic Res 151, 33–49 (2020). https://doi.org/10.1007/s11205-020-02363-2

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