@inbook{7e230e258e07401d90027b2db064eddf,
title = "Location Inference for Non-geotagged Tweets in User Timelines [Extended Abstract]",
abstract = "This study explores the problem of inferring locations for individual tweets. We scrutinize Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. Extensive experiments on a large set of real Twitter data suggest that our models are effective at inferring locations for non-geotagged tweets and outperform the state-of-the-art approaches significantly in terms of inference accuracy.",
keywords = "Bayes, Location inference, Lstm, Twitter",
author = "Pengfei Li and Hua Lu and Nattiya Kanhabua and Sha Zhao and Gang Pan",
year = "2019",
doi = "10.1109/ICDE.2019.00250",
language = "English",
isbn = "978-1-5386-7474-1",
series = "Proceedings of the International Conference on Data Engineering",
publisher = "IEEE",
pages = "2111--2112",
booktitle = "Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE)",
address = "United States",
note = "The 35th IEEE International Conference on Data Engineering (ICDE), ICDE 2019 ; Conference date: 08-04-2019 Through 12-04-2019",
}