Geotagging tweets to landmarks using Convolutional Neural Networks with text and posting time

Kwan Hui Lim, Shanika Karunasekera, Aaron Harwood, Yasmeen George

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of IUI 2019
EditorsOliver Brdiczka, Gaelle Calvary, Polo Chau
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages61-62
Number of pages2
ISBN (Electronic)9781450366731
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventInternational Conference on Intelligent User Interfaces 2019 - Los Angeles, United States of America
Duration: 16 Mar 201920 Mar 2019
Conference number: 24th
http://iui.acm.org/2019/

Conference

ConferenceInternational Conference on Intelligent User Interfaces 2019
Abbreviated titleIUI 2019
Country/TerritoryUnited States of America
CityLos Angeles
Period16/03/1920/03/19
Internet address

Keywords

  • Geolocation
  • Geotagging
  • Neural networks
  • Twitter

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