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
$\sim$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.

研究了社交媒体文本与所发布地点类型之间的关系，提出了一个包含大约 200,000 条英文推文的新数据集，用于预测推文所发布的位置类型，并训练分类器来预测推文所发送的位置类型，能够达到宏观 F1 值 43.67，并揭示了与每种类型场所相关的语言标记，这种能够从推文中预测语义场所信息的能力在推荐系统、个性化服务和文化地理学中应用广泛。