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Facility Detection and Popularity Assessment from Text Classification of Social Media and Crowdsourced Data...

by Kevin A Sparks, Roger G Li, Gautam Thakur, Robert N Stewart, Marie L Urban
Publication Type
Conference Paper
Book Title
ACM SigSpatial Geographic Information Retrieval 2016
Publication Date
Page Number
Publisher Location
District of Columbia, United States of America
Conference Name
ACM SigSpatial Geographic Information Retrieval 2016
Conference Location
San Francisco, California, United States of America
Conference Date

Advances in technology have continually progressed our understanding of where people are, how they use the environment around them, and why they are at their current location. Having a better knowledge of when various locations become popular through space and time could have large impacts on research fields like urban dynamics and energy consumption. In this paper, we discuss the ability to identify and locate various facility types (e.g. restaurant, airport, stadiums) using social media, and assess methods in determining when these facilities become popular over time. We use natural language processing tools and machine learning classifiers to interpret geotagged Twitter text and determine if a user is seemingly at a location of interest when the tweet was sent. On average our classifiers are approximately 85% accurate varying across multiple facility types, with a peak precision of 98%. By using these methods to classify unstructured text, geotagged social media data can be an extremely useful tool to better understanding the composition of places and how and when people use them.