Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2017

Abstract

Which venue is a tweet posted from? We referred this as fine-grained geolocation. To solve this problem effectively, we develop novel techniques to exploit each posting user's content history. This is motivated by our finding that most users do not share their visitation history, but have ample content history from tweet posts. We formulate fine-grained geolocation as a ranking problem whereby given a test tweet, we rank candidate venues. We propose several models that leverage on three types of signals from locations, users and peers. Firstly, the location signals are words that are indicative of venues. We propose a location-indicative weighting scheme to capture this. Next we exploit user signals from each user's content history to enrich the very limited content of their tweets which have been targeted for geolocation. The intuition is that the user's other tweets may have been from the test venue or related venues, thus providing informative words. In this regard, we propose query expansion as the enrichment approach. Finally, we exploit the signals from peer users who have similar content history and thus potentially similar visitation behavior as the users of the test tweets. This suggests collaborative filtering where visitation information is propagated via content similarities. We proposed several models incorporating different combinations of the three signals. Our experiments show that the best model incorporates all three signals. It performs 6% to 40% better than the baselines depending on the metric and dataset.

Keywords

Query expansion, Tweet geolocation, Collaborative filtering

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, November 6-10

First Page

1279

Last Page

1288

ISBN

9781450349185

Identifier

10.1145/3132847.3132906

Publisher

ACM

City or Country

New York

Embargo Period

3-4-2018

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3132847.3132906

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