Predicting Asthma Prevalence by Linking Social Media Data and Traditional Surveys

Hongying Dai, Brian R. Lee and Jianqiang Hao

The ANNALS of the American Academy of Political and Social Science, 2017, vol. 669, issue 1, 75-92

Abstract: Asthma is one of the most common chronic diseases that has a profound impact on people’s well-being and our society. In this study, we link multiple large-scale data sources to construct an epidemiological model to predict asthma prevalence across geographic regions. We use: (1) the Social Media Monitoring (SMM) data from Twitter ( N = 500 million tweets/day), (2) the 2014 Behavioral Risk Factor Surveillance System (BRFSS) ( N = 464,664), and (3) the 2014 American Community Survey (ACS) conducted by the U.S. Census Bureau ( N = 3.5 million per year). We predict asthma prevalence in the traditional survey (BRFSS) using social media information collected from Twitter and socioeconomic factors collected from ACS. The evidence suggests that monitoring asthma-related tweets may provide real-time information that can be used to predict outcomes from traditional surveys.

Keywords: asthma; social media monitoring; SMM; ACS; BRFSS; data linkage (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:anname:v:669:y:2017:i:1:p:75-92

DOI: 10.1177/0002716216678399

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