Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US

Mining the social media outlet Twitter for geolocated messages provides a rich database of information on people's thoughts and sentiments about myriad topics, like public health. Examining this spatial data has been particularly useful to researchers interested in monitoring and mapping disease outbreaks, like influenza. However, very little has been done to utilize this massive resource to examine other public health issues. This paper uses an advanced data-mining framework with a novel use of social media data retrieval and sentiment analysis to understand how geolocated tweets can be used to explore the prevalence of healthy and unhealthy food across the contiguous United States. Additionally, tweets are associated with spatial data provided by the US Department of Agriculture (USDA) of low-income, low-access census tracts (e.g. food deserts), to examine whether tweets about unhealthy foods are more common in these disadvantaged areas. Results show that these disadvantaged census tracts tend to have both a lower proportion of tweets about healthy foods with a positive sentiment, and a higher proportion of unhealthy tweets in general. These findings substantiate the methods used by the USDA to identify regions that are at risk of having low access to healthy foods.