Modeling Food Popularity Dependencies using Social Media data

TL;DR

This paper presents a machine learning approach to analyze and model the spatio-temporal popularity of different cuisines using social media data, with a focus on Manhattan, NYC.

Contribution

It introduces a method combining Kernel Density Estimation and Bayesian Networks to identify food trends and dependencies from geo-tagged social media data.

Findings

Identified hot spots of cuisine popularity in Manhattan

Modeled dependencies among different food types

Demonstrated approach's applicability to other regions

Abstract

The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.