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Tracking Geographical Locations using a Geo-Aware Topic Model for Analyzing Social Media Data
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. FOI Swedish Defence Research Agency, Sweden.
KTH, School of Computer Science and Communication (CSC). Google, Inc., United States.
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS. FOI Swedish Defence Research Agency, Sweden.ORCID iD: 0000-0002-2677-9759
2017 (English)In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 99, no SI, p. 18-29Article in journal (Refereed) Published
Abstract [en]

Tracking how discussion topics evolve in social media and where these topics are discussed geographically over time has the potential to provide useful information for many different purposes. In crisis management, knowing a specific topic’s current geographical location could provide vital information to where, or even which, resources should be allocated. This paper describes an attempt to track online discussions geographically over time. A distributed geo-aware streaming latent Dirichlet allocation model was developed for the purpose of recognizing topics’ locations in unstructured text. To evaluate the model it has been implemented and used for automatic discovery and geographical tracking of election topics during parts of the 2016 American presidential primary elections. It was shown that the locations correlated with the actual election locations, and that the model provides a better geolocation classification compared to using a keyword-based approach.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 99, no SI, p. 18-29
Keywords [en]
Social media, Topic modeling, Geo-awareness, Trend analysis, Latent Dirichlet allocation, Streaming media
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-210570DOI: 10.1016/j.dss.2017.05.006ISI: 000405162500003Scopus ID: 2-s2.0-85020801622OAI: oai:DiVA.org:kth-210570DiVA, id: diva2:1118834
Funder
EU, FP7, Seventh Framework Programme, 312649
Note

QC 20170704

Available from: 2017-07-02 Created: 2017-07-02 Last updated: 2018-01-13Bibliographically approved

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García Lozano, MarianelaSchreiber, JonahBrynielsson, Joel
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