Point-of-Interest Type Inference from Social Media Text (2020)

Abstract: Physical places help shape how we perceive the experiences we have there. We study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of ~200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.

## introduction social networks such as twitter allow users to share information about different aspects of their lives including feelings and experiences from places that they visit, from local restaurants to sport stadiums and parks. feelings and emotions triggered by performing an activity or living an experience in a point-of-interest (poi) can give a glimpse of the atmosphere in that place @xcite . in particular, the language used in posts from pois is an important component that contributes toward the place's identity and has been extensively studied in the context of social and cultural geography @xcite @xcite . social media posts from a particular location are usually focused on the person posting the content, rather than on providing explicit information about the place. in this paper, we aim to study the language that people on twitter use to share information about a specific place they are visiting. thus, we define the prediction of a poi type given a post (i.e. tweet) as a multi-class classification task using only information available at posting time. given the text from a user's post, our goal is to predict the correct type of the location it was posted, e.g. park, bar or shop. inferring the type of place from a user's post using linguistic information, is useful for cultural geographers to study a place's identity @xcite and has downstream geosocial applications such as poi visualisation @xcite and recommendation @xcite @xcite . predicting the type of a poi is inherently different to predicting the poi type from comments or reviews. the role of the latter is to provide opinions or descriptions of the places, rather than the activities and feelings of the user posting the text @xcite , as illustrated in our contributions are as follows: (1) we provide the first study of poi type prediction in computational linguistics; (2) a large data set made out of ## point-of-interest type data we define the poi type prediction as a multi-class classification task performed at the social media post level. given a post t, defined as a sequence of tokens @xmath0, ..., t n }, the goal is to label t as one of the m poi categories. we create a novel data set for poi type prediction containing text and the location type it was posted from as, to the best of our knowledge, no such data set is available. we use twitter as our data source because it contains a large variety of linguistic information such as expression of thoughts, opinions and emotions @xcite . ## analysis we first analyze our data set to understand the relationship between location type, language and posting time. ## conclusion we presented the first study on predicting the poi type a social media message was posted from and developed a large-scale data set with tweets mapped to their poi category. we conducted an analysis to uncover features specific to place type and trained predictive models to infer the poi category using only tweet text and posting time with accuracy close to 50% across eight categories. future work will focus on using other modalities such as network @xcite or image information @xcite and prediction at a more granular level of poi types.