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Tweet-tales: moods of socio-economic crisis?

Author

Listed:
  • Grazia Biorci

    (CNR-Ircres, Genova)

  • Antonella Emina

    (CNR-Ircres, Moncalieri)

  • Michelangelo Puliga

    (IMT School for Advanced studies Lucca)

  • Lisa Sella

    (CNR-Ircres, Moncalieri)

  • Gianna Vivaldo

    (IMT School for Advanced studies Lucca)

Abstract
The widespread adoption of highly interactive social media like Twitter, Facebook and other platforms allow users to communicate moods and opinions to their social network. Those platforms represent an unprecedented source of information about human habits and socio-economic interactions. Several new studies have started to exploit the potential of these big data as fingerprints of economic and social interactions. The present analysis aims at exploring the informative power of indicators derived from social media activity, with the aim to trace some preliminary guidelines to investigate the eventual correspondence between social media indices and available labour market indicators at a territorial level. The study is based on a large dataset of about 4 million Italian-language tweets collected from October 2014 to December 2015, filtered by a set of specific keywords related to the labour market. With techniques from machine learning and user’s geolocalization, we were able to subset the tweets on specific topics in all Italian provinces. The corpus of tweets is then analyzed with linguistic tools and hierarchical clustering analysis. A comparison with traditional economic indicators suggests a strong need for further cleaning procedures, which are then developed in detail. As data from social networks are easy to obtain, this represents a very first attempt to evaluate their informative power in the Italian context, which is of potentially high importance in economic and social research.

Suggested Citation

  • Grazia Biorci & Antonella Emina & Michelangelo Puliga & Lisa Sella & Gianna Vivaldo, 2016. "Tweet-tales: moods of socio-economic crisis?," Working Papers 04/2016, IMT School for Advanced Studies Lucca, revised Jul 2016.
    • Handle: RePEc:ial:wpaper:04/2016
    as

    Download full text from publisher

    File URL: https://eprints.imtlucca.it/3519/1/EIC_WP_4_2016.pdf
    File Function: First version, 2016
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    References listed on IDEAS

    as
    1. 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.
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    5. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Big data; social media; Twitter; hierarchical clustering; unemployment;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity

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