Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2018

Abstract

Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely to visit venues near their previous visits. We also find that a substantial proportion of users post one or more geocoded tweet(s), thus providing their location history data. We then propose geolocation models that exploit spatial homophily and spatial focus characteristics plus posting time information. Our models rank candidate venues of test tweets such that the actual posting venue is ranked high. To better tune model parameters, we introduce a learning-to-rank framework. Our best model significantly outperforms state-of-the-art baselines. Furthermore, we show that tweets without any location-indicative words can be geolocated meaningfully as well.

Keywords

Tweet geolocation, Learning to rank, Spatial homophily, Spatial focus

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Information Systems

Volume

36

Issue

3

First Page

26:1

Last Page

34

ISSN

1046-8188

Identifier

10.1145/3156667

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3156667

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