Tweet-tales: moods of socio-economic crisis?

Grazia Biorci (), Antonella Emina (), Michelangelo Puliga (), Lisa Sella () and Gianna Vivaldo ()
Additional contact information
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

No 04/2016, Working Papers from 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.

Keywords: Big data; social media; Twitter; hierarchical clustering; unemployment (search for similar items in EconPapers)
JEL-codes: C4 C49 C55 C81 E24 (search for similar items in EconPapers)
Pages: 12
Date: 2016-07, Revised 2016-07
New Economics Papers: this item is included in nep-mac and nep-soc
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in EIC working paper series

Downloads: (external link)
https://eprints.imtlucca.it/3519/1/EIC_WP_4_2016.pdf First version, 2016 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ial:wpaper:04/2016

Access Statistics for this paper

More papers in Working Papers from IMT School for Advanced Studies Lucca Contact information at EDIRC.
Bibliographic data for series maintained by Leonardo Mezzina ().