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Every tweet counts? How sentiment analysis of social networks can improve our knowledge of citizens’ policy preferences. An application to Italy and France

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Listed:
  • Stefano Maria IACUS
  • Giuseppe PORRO
  • Luigi CURINI
  • Andrea CERON

Abstract

The growing usage of social media by a wider audience of citizens sharply increases the possibility to investigate the web as a device to explore and track policy preferences. In the present paper we apply the recent method proposed in Hopkins and King (2010) to three different scenarios, by analyzing on one side the on-line popularity of Italian political leaders throughout 2011, and on the other the voting intention of French internet-users in both the 2012 Presidential ballot and in the subsequent Legislative election. Despite internet users are not necessarily representative of the whole population of country’s citizens, our analysis shows a remarkable ability of social-media to forecast electoral results as well as a noteworthy correlation between social-media and traditional mass surveys results. We also illustrate that the predictive ability of social-media analysis strengthens as the number of citizens’ expressing on-line their opinion increases, provided they act consistently on that (i.e. apart from high abstention rates).

Suggested Citation

  • Stefano Maria IACUS & Giuseppe PORRO & Luigi CURINI & Andrea CERON, 2012. "Every tweet counts? How sentiment analysis of social networks can improve our knowledge of citizens’ policy preferences. An application to Italy and France," Departmental Working Papers 2012-19, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2012-19
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    References listed on IDEAS

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    1. Oliver Falck & Robert Gold & Stephan Heblich, 2014. "E-lections: Voting Behavior and the Internet," American Economic Review, American Economic Association, vol. 104(7), pages 2238-2265, July.
    2. Anthony Downs, 1957. "An Economic Theory of Political Action in a Democracy," Journal of Political Economy, University of Chicago Press, vol. 65, pages 135-135.
    3. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
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    Cited by:

    1. Luigi Curini & Stefano Iacus & Luciano Canova, 2015. "Measuring Idiosyncratic Happiness Through the Analysis of Twitter: An Application to the Italian Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 121(2), pages 525-542, April.

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