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• W2019331423 abstract "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 unstructured and noisy geographic information in social media into a high-dimensional multivariate signal of geographic semantics. Then, we use dimensionality reduction to extract latent 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 extracting sparse latent features find dimensions with higher informational value. In addition, we show that prior normalization 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." @default.
• W2019331423 created "2016-06-24" @default.
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• W2019331423 date "2012-11-06" @default.
• W2019331423 modified "2023-09-25" @default.
• W2019331423 title "Latent geographic feature extraction from social media" @default.
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• W2019331423 doi "https://doi.org/10.1145/2424321.2424342" @default.
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