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Geotagging tweets to landmarks using convolutional neural networks with text and posting time
Conference proceeding

Geotagging tweets to landmarks using convolutional neural networks with text and posting time

Kwan Hui Lim, Shanika Karunasekera, Aaron Harwood, Yasmeen George and ACM
Companion Proceedings of the 24th International Conference on Intelligent User Interfaces, pp.61-62
ACM Conferences
IUI '19: 24th International Conference on Intelligent User Interfaces
16/03/2019
DOI: https://doi.org/10.1145/3308557.3308691

Abstract

Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools -- Social networking sites Information systems -- Information systems applications -- Spatial-temporal systems -- Location based services
Geotagged tweets serve many important applications, e.g., crisis management, but only a small proportion of tweets are explicitly geotagged. We propose a Convolutional Neural Network (CNN) architecture for geotagging tweets to landmarks, based on the text in tweets and other meta information, such as posting time and source. Using a dataset of Melbourne tweets, experimental results show that our algorithm out-performed various state-of-the-art baselines.

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