Tracking health and nutrition signals from social media data

Description

This project will explore the ability to track real-life health and nutrition signals from social media data, focusing on data from Instagram and Foursquare. We will investigate the quality of Instagram posts as a source of data for measurements of dietary patterns and nutrition quality, focusing on spatial, textual, and (new in this semester) image content of posts linked to food outlets in Los Angeles, as well as nutritional content analysis of menus available online. Multiple aims will be investigated in this project, including: scraping data from social media; NLP of tag, comments, and menu data; image analysis; predictive models and social network analysis; and more. Also new in this semester: “ground truth” data on dietary patterns of LA residents will be available, enabling validation of dietary measures and predictive models built from Instagram posts.

The project will build on the DataFest 2019 project, and will expand the scope to actually access up-to-date data from Instagram, in particular: data with images, the underlying social connections / social network, and of course more timely (which requires data scraping).

Students

• Abhilash Karpurapu

• Erica Xia

• Iris Liu

• Spoorti Nidagundi

Advisors

• Andrés Abeliuk

• Abigail Horn

• Kayla de la Haye

• Yelena Mejova

Skills Required by the team

• Python
• Machine Learning
• NLP
• Statistical Analysis
• Social Network Analysis
• Sentiment Analysis
• R
• Image Analysis