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Pairing Tweets with the Right Location

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Digital Economy. Emerging Technologies and Business Innovation (ICDEc 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 431))

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Abstract

Twitter is used to provide location-relevant information and event updates. It is important to identify location-relevant tweets in order to harness location-relevant information and event updates from Twitter. However, the identification of location-relevant tweets is a challenging problem as the location names are not always explicit. Instead, mostly the location names are implicitly embedded in tweets. This research proposes a novel approach, labelled as DigiCities, to add geographical context to non-geo tagged tweets. The proposed approach helps in improving identification of location-relevant tweet by harnessing the location-specific information embedded in user-ids and hashtags included in tweets. Tweets relevant to eight cities were identified and used in classification experiments, and the use of DigiCities improved the overall classification accuracy of tweets into relevant city classes.

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    http://www.cs.waikato.ac.nz/ml/weka/.

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Authors and Affiliations

  1. Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada

    Esha & Osmar Zaïane

Authors
  1. Esha
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  2. Osmar Zaïane
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Correspondence to Esha or Osmar Zaïane .

Editor information

Editors and Affiliations

  1. University of Manouba, Manouba, Tunisia

    Rim Jallouli

  2. University of Manouba, Manouba, Tunisia

    Mohamed Anis Bach Tobji

  3. University of Quebec at Chicoutimi, Chicoutimi, QC, Canada

    Hamid Mcheick

  4. Tallinn University of Technology, Tallinn, Estonia

    Gunnar Piho

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Esha, Zaïane, O. (2021). Pairing Tweets with the Right Location. In: Jallouli, R., Bach Tobji, M.A., Mcheick, H., Piho, G. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2021. Lecture Notes in Business Information Processing, vol 431. Springer, Cham. https://doi.org/10.1007/978-3-030-92909-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-92909-1_18

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