Automatic Location Type Classification From Social-Media Posts

We introduce the problem of Automatic Location Type Classification from social media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with their corresponding location type, e.g., school, church, restaurant or museum. We provide a dataset of locations associated with tweets posted in close geographical proximity. We explore two approaches to the problem: (a) a pipeline approach where each message is first classified, and then the location associated with the message set is inferred from the individual message labels; and (b) a joint approach where the individual messages are simultaneously processed to yield the desired location type. Our results demonstrate the superiority of the joint approach. Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, simpler linear classifiers outperform deep neural network alternatives that have shown superior in previous text classification tasks.