From twitter to GDP: Estimating economic activity from social media

Agustín Indaco

Regional Science and Urban Economics, 2020, vol. 85, issue C

Abstract: Using all geo-located image tweets shared on Twitter in 2012–2013, I find that the volume of tweets is a valid proxy for estimating GDP at the country level, explaining 78 percent of cross-country variations. I also exploit the geographic granularity of social media posts to estimate and predict GDP at the sub-national level. I find that tweets alone can explain 52 percent of the variation in GDP across cities in the US. Estimates using Twitter data perform on par with the more common night-lights proxy. Furthermore, both indicators seem to capture different aspects of economic activity and thus complement each other.

Keywords: Social media data; Big data; Cities; Satellite images; National accounts (search for similar items in EconPapers)
JEL-codes: C53 C55 E01 O11 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Working Paper: From Twitter to GDP: Estimating Economic Activity From Social Media (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:85:y:2020:i:c:s0166046220302763

DOI: 10.1016/j.regsciurbeco.2020.103591

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