Location Inference for Non-Geotagged Tweets in User Timelines [Extended Abstract]

Pengfei Li, Hua Lu, Nattiya Kanhabua, Sha Zhao, Gang Pan

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

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.
OriginalsprogEngelsk
Titel35th IEEE International Conference on Data Engineering, ICDE 2019
Antal sider2
ForlagIEEE
Publikationsdato2019
Sider2111-2112
ISBN (Trykt)978-1-5386-7475-8
ISBN (Elektronisk)978-1-5386-7474-1
DOI
StatusUdgivet - 2019
Udgivet eksterntJa
Begivenhed35th International Conference on Data Engineering (ICDE) - Macau, Kina
Varighed: 8 apr. 201911 apr. 2019
Konferencens nummer: 35
https://www.computer.org/csdl/proceedings/icde/2019/1aDSOMTGCIw

Konference

Konference35th International Conference on Data Engineering (ICDE)
Nummer35
LandKina
ByMacau
Periode08/04/201911/04/2019
Internetadresse
NavnIEEE Transactions on Knowledge and Data Engineering
ISSN1041-4347

Citer dette

Li, P., Lu, H., Kanhabua, N., Zhao, S., & Pan, G. (2019). Location Inference for Non-Geotagged Tweets in User Timelines [Extended Abstract]. I 35th IEEE International Conference on Data Engineering, ICDE 2019 (s. 2111-2112). IEEE. IEEE Transactions on Knowledge and Data Engineering https://doi.org/10.1109/ICDE.2019.00250