Conference Proceeding Article
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.
Databases and Information Systems | Social Media | Theory and Algorithms
Data Science and Engineering
Proceedings of the 11th AAAI Conference on Web and Social Media ICWSM 2017: Montreal, Canada, May 15-18
City or Country
Menlo Park, CA
CHONG, Wen Haw and Ee-peng LIM.
Exploiting contextual information for fine-grained tweet geolocation. (2017). Proceedings of the 11th AAAI Conference on Web and Social Media ICWSM 2017: Montreal, Canada, May 15-18. 488-491. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3656
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