Publication Type

Conference Paper

Publication Date



The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we also utilize the properties of (1) spatial focus where users are more likely to visit venues near each other and (2) spatial homophily where venues near each other tend to share more similar tweet content, compared to venues further apart. Our proposed model significantly outperforms the content-only approaches.


Social Media | Theory and Algorithms

Research Areas

Data Management and Analytics


The 11th International AAAI Conference on Web and Social Media


University of Sydney, Institute of Criminology

City or Country

Montreal, Canada

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.