Tracking health and nutrition signals from social media data (begun Spring 2020)
Description
Food environments (the physical spaces where people acquire and consume food) can profoundly impact diet and related diseases. Effective, robust measures of food environment nutritional quality are required by researchers and policymakers investigating their effects on individual dietary behavior and designing targeted public health interventions. The most commonly used indicators of food environment nutritional quality are limited to measuring the binary presence or absence of entire categories of food outlet type, such as ‘fast-food’ outlets, which can range from burger joints to salad chains. There would be great value in a summarizing indicator of restaurant nutritional quality that exists along a continuum, and which can be applied at the scale of large food environments, for example across Los Angeles County, to make distinctions between diverse restaurants within and across categories of food outlets.
This project will explore the ability to track real-life health and nutrition signals from social media data, focusing on data from Foursquare and Yelp. We will investigate the ability to access menu information from the APIs of these social media platforms, and develop measures to assess the nutritional content of these menus. Multiple aims will be investigated in this project, including scraping data from social media; NLP of menu text, tag, and comment data; developing predictive models of obesity; and more. “Ground truth” data on dietary patterns of LA residents will be available, enabling validation of dietary measures and predictive models built from menu data.
Students
Advisors
Skills Required by the team
- Python
- R
- NLP
- Statistical Modeling