Latent Geographic Feature Extraction from Social Media

In this work we present a framework for the unsupervised extraction of latent geographic features from georeferenced social media. A geographic feature represents a semantic dimension of a location and can be seen as a sensor that measures a signal of geographic semantics. Our goal is to extract a small number of informative geographic features from social media, to describe and explore geographic space, and for subsequent spatial analysis, e.g., in market research. We propose a framework that, first, transforms the unstruc-tured and noisy geographic information in social media into a high-dimensional multivariate signal of geographic seman-tics. Then, we use dimensionality reduction to extract la-tent geographic features. We conduct experiments using two large-scale Flickr data sets covering the LA area and the US. We show that dimensionality reduction techniques ex-tracting sparse latent features find dimensions with higher informational value. In addition, we show that prior nor-malization can be used as a parameter in the exploration process to extract features representing different geographic characteristics, that is, landmarks, regional phenomena, or global phenomena