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An Unsupervised Approach to Identify Location Based on the Content of User’s Tweet History

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Active Media Technology (AMT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8610))

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Abstract

We propose and evaluate an unsupervised approach to identify the location of a user purely based on tweet history of that user. We combine the location references from tweets of a user with gazetteers like DBPedia to identify the geolocation of that user at a city level. This can be used for location based personalization services like targeted advertisements, recommendations and services on a finer level. In this paper, we use convex hull and k-center clustering, to identify the location of a user at a city level. The main contributions of this paper are: (i) reliability on just the contents of a tweet, without the need for manual intervention or training data; (ii) a novel approach to handle ambiguous location entries; and (iii) a computational geometric solution to narrow down the location of the user from a set of points corresponding to location references. Experimental results show that the system is able to identify a location for each user with high accuracy within a tolerance range. We also study the effect of tolerance on accuracy and average error distance.

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Author information

Authors and Affiliations

  1. The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, USA

    Satya Katragadda, Miao Jin & Vijay Raghavan

Authors
  1. Satya Katragadda
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  2. Miao Jin
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  3. Vijay Raghavan
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślȩzak

  2. Department of Computer Science, Loughborough University, Loughborough, U.K.

    Gerald Schaefer

  3. Computer Science Department, University of British Columbia, 2366 Main Mall, P.O. Box, Vancouver, B.C., Canada

    Son T. Vuong

  4. Department of Information & Communication Engineering, Inha University, Korea

    Yoo-Sung Kim

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Katragadda, S., Jin, M., Raghavan, V. (2014). An Unsupervised Approach to Identify Location Based on the Content of User’s Tweet History. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, YS. (eds) Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham. https://doi.org/10.1007/978-3-319-09912-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-09912-5_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09911-8

  • Online ISBN: 978-3-319-09912-5

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