From twitter to GDP: Estimating economic activity from social media
研究发现2012-2013年推特上带地理位置的图片推文数量能有效估算国家层面的GDP，解释78%的跨国差异；在美国城市层面，推文数据可解释52%的GDP差异，其表现与夜间灯光数据相当且互补。
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.