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Spatio-Temporal Multiple Geo-Location Identification on Twitter
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0003-1007-8533
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2018 (English)In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 / [ed] Abe, N Liu, H Pu, C Hu, X Ahmed, N Qiao, M Song, Y Kossmann, D Liu, B Lee, K Tang, J He, J Saltz, J, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3412-3421Conference paper, Published paper (Refereed)
Abstract [en]

Twitter Geo-tags that indicate the exact location of messages have many applications from localized opinion mining during elections to efficient traffic management in critical situations. However, less than 6% of Tweets are Geo-tagged, which limits the implementation of those applications. There are two groups of solutions: content and network-based. The first group uses location indicative factors like URLs and topics, extracted from the content of tweets, to infer Geo-location for non geoactive users, whereas the second group benefits from friendship ties in the underlying social network graph. Friendship ties are better predictors compared to content information because they are less noisy and often follow the natural human spatial movement patterns. However, their prediction's accuracy is still limited because they ignore the temporal aspects of human behavior and always assume a single location per user. This research aims to extend the current network-based approaches by taking users' temporal dimension into account. We assume multiple locations per user during different time-slots and hypothesize that location predictability varies depending on the time and the properties of the social membership group. Thus, we propose a hierarchical solution to apply temporal categorizations on top of social network partitioning for multiple location prediction for users in Online Social Networks (OSNs) like Twitter. Given a largescale Twitter dataset, we show that users' location predictability exhibits different behavior in different time-slots and different social groups. We find that there are specific conditions where users are more predictable in terms of Geo-location. Our solution outperforms the state-of-the-art by improving the prediction accuracy by 16:6% in terms of Median Error Distance (MED) over the same recall.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 3412-3421
Series
IEEE International Conference on Big Data, ISSN 2639-1589
Keywords [en]
Geo-Location Identification, Graph Partitioning, Social Network Analysis, Spatio-Temporal Analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254147DOI: 10.1109/BigData.2018.8621997ISI: 000468499303064Scopus ID: 2-s2.0-85062605032ISBN: 978-1-5386-5035-6 (print)OAI: oai:DiVA.org:kth-254147DiVA, id: diva2:1329207
Conference
2018 IEEE International Conference on Big Data, Big Data 2018; Seattle; United States; 10 December 2018 through 13 December 2018
Note

QC 20190624

Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-11-19Bibliographically approved
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Ghoorchian, KambizGirdzijauskas, Sarunas

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