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Big Data sources and methods for social and economic analyses

Author

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  • Blazquez, Desamparados
  • Domenech, Josep

Abstract

The Data Big Bang that the development of the ICTs has raised is providing us with a stream of fresh and digitized data related to how people, companies and other organizations interact. To turn these data into knowledge about the underlying behavior of the social and economic agents, organizations and researchers must deal with such amount of unstructured and heterogeneous data. Succeeding in this task requires to carefully plan and organize the whole process of data analysis taking into account the particularities of the social and economic analyses, which include the wide variety of heterogeneous sources of information and a strict governance policy. Grounded on the data lifecycle approach, this paper develops a Big Data architecture that properly integrates most of the non-traditional information sources and data analysis methods in order to provide a specifically designed system for forecasting social and economic behaviors, trends and changes.

Suggested Citation

  • Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
  • Handle: RePEc:eee:tefoso:v:130:y:2018:i:c:p:99-113
    DOI: 10.1016/j.techfore.2017.07.027
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    References listed on IDEAS

    as
    1. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    2. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    3. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    4. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    5. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
    6. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    7. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    8. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    9. Concha Artola & Fernando Pinto & Pablo de Pedraza García, 2015. "Can internet searches forecast tourism inflows?," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 103-116, April.
    10. Sanjay K. Arora & Yin Li & Jan Youtie & Philip Shapira, 2016. "Using the wayback machine to mine websites in the social sciences: A methodological resource," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(8), pages 1904-1915, August.
    11. Sonal S. Pandya & Rajkumar Venkatesan, 2016. "French Roast: Consumer Response to International Con flict--Evidence from Supermarket Scanner Data," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 42-56, March.
    12. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    13. repec:spr:scient:v:95:y:2013:i:3:d:10.1007_s11192-013-0950-7 is not listed on IDEAS
    14. Justin Malbon, 2013. "Taking Fake Online Consumer Reviews Seriously," Journal of Consumer Policy, Springer, vol. 36(2), pages 139-157, June.
    15. repec:spr:scient:v:90:y:2012:i:1:d:10.1007_s11192-011-0508-5 is not listed on IDEAS
    16. repec:eee:touman:v:46:y:2015:i:c:p:311-321 is not listed on IDEAS
    17. Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
    18. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    19. repec:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1434-0 is not listed on IDEAS
    20. Frota Neto, João Quariguasi & Bloemhof, Jacqueline & Corbett, Charles, 2016. "Market prices of remanufactured, used and new items: Evidence from eBay," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 371-380.
    21. repec:eee:touman:v:46:y:2015:i:c:p:454-464 is not listed on IDEAS
    22. Harsanyi, John C, 1978. "Bayesian Decision Theory and Utilitarian Ethics," American Economic Review, American Economic Association, vol. 68(2), pages 223-228, May.
    23. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    24. Nikolaos Askitas & Klaus F. Zimmermann, 2013. "Nowcasting Business Cycles Using Toll Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 299-306, July.
    25. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    26. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    27. Chris Hand & Guy Judge, 2012. "Searching for the picture: forecasting UK cinema admissions using Google Trends data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1051-1055, July.
    28. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    29. Mavragani, Amaryllis & Tsagarakis, Konstantinos P., 2016. "YES or NO: Predicting the 2015 GReferendum results using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 1-5.
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    1. repec:eee:tefoso:v:144:y:2019:i:c:p:221-232 is not listed on IDEAS
    2. repec:eee:tefoso:v:144:y:2019:i:c:p:573-584 is not listed on IDEAS

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