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Associating type to locations can be used to enrich maps and can serve aplethora of geospatial applications. An automatic method to do so could makethe process less expensive in terms of human labor, and faster to react tochanges. In this paper we study the problem of Geosocial LocationClassification, where the type of a site, e.g., a building, is discovered basedon social-media posts. Our goal is to correctly associate a set of messagesposted in a small radius around a given location with the correspondinglocation type, e.g., school, church, restaurant or museum. We explore twoapproaches to the problem: (a) a pipeline approach, where each message is firstclassified, and then the location associated with the message set is inferredfrom the individual message labels; and (b) a joint approach where theindividual messages are simultaneously processed to yield the desired locationtype. We tested the two approaches over a dataset of geotagged tweets. Ourresults demonstrate the superiority of the joint approach. Moreover, we showthat due to the unique structure of the problem, where weakly-related messagesare jointly processed to yield a single final label, linear classifiersoutperform deep neural network alternatives.

Geosocial Location Classification: Associating Type to Places Based on Geotagged Social-Media Posts