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Building Decision-making Indicators Through Network Analysis of Big Data

Author

Listed:
  • Venera Tomaselli

    (University of Catania 8)

  • Giovanni Giuffrida

    (University of Catania 8)

  • Simona Gozzo

    (University of Catania 8)

  • Francesco Mazzeo Rinaldi

    (University of Catania 8
    KTH, Royal Institute of Technology)

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.

Suggested Citation

  • Venera Tomaselli & Giovanni Giuffrida & Simona Gozzo & Francesco Mazzeo Rinaldi, 2020. "Building Decision-making Indicators Through Network Analysis of Big Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 33-49, August.
    • Handle: RePEc:spr:soinre:v:151:y:2020:i:1:d:10.1007_s11205-020-02363-2
    DOI: 10.1007/s11205-020-02363-2
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.
    3. Enrico di Bella & Lucia Leporatti & Filomena Maggino, 2018. "Big Data and Social Indicators: Actual Trends and New Perspectives," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(3), pages 869-878, February.
    4. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    5. Hildrun Kretschmer & Isidro F. Aguillo, 2004. "Visibility of collaboration on the Web," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 405-426, November.
    6. Yufang Peng & Jin Shi & Marcelo Fantinato & Jing Chen, 2017. "A study on the author collaboration network in big data," Information Systems Frontiers, Springer, vol. 19(6), pages 1329-1342, December.
    7. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    8. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
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