# Point-of-Interest Type Inference from Social Media Text

arXiv: https://arxiv.org/abs/2009.14734 · HTML: https://mlsanity.com/paper/2009.14734/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Automatic Identification and Classification of Bragging in Social Media](https://mlsanity.com/paper/2203.05840/) (2022)
- [Point-of-Interest Type Prediction using Text and Images](https://mlsanity.com/paper/2109.00602/) (2021)
- [Stability of Syntactic Dialect Classification Over Space and Time](https://mlsanity.com/paper/2209.04958/) (2022)
- [A Multimodal Analysis of Influencer Content on Twitter](https://mlsanity.com/paper/2309.03064/) (2023)
- [Predicting the Geolocation of Tweets Using transformer models on Customized Data](https://mlsanity.com/paper/2303.07865/) (2023)

## Abstract (arXiv)

Physical places help shape how we perceive the experiences we have there. For the first time, 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.