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Physical places help shape how we perceive the experiences we have there. Forthe first time, we study the relationship between social media text and thetype of the place from where it was posted, whether a park, restaurant, orsomeplace else. To facilitate this, we introduce a novel data set of$\sim$200,000 English tweets published from 2,761 different points-of-interestin the U.S., enriched with place type information. We train classifiers topredict the type of the location a tweet was sent from that reach a macro F1 of43.67 across eight classes and uncover the linguistic markers associated witheach type of place. The ability to predict semantic place information from atweet has applications in recommendation systems, personalization services andcultural geography.

Point-of-Interest Type Inference from Social Media Text