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On Predicting Geolocation of Tweets Using Convolutional Neural Networks

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10354))

Abstract

In many Twitter studies, it is important to know where a tweet came from in order to use the tweet content to study regional user behavior. However, researchers using Twitter to understand user behavior often lack sufficient geo-tagged data. Given the huge volume of Twitter data there is a need for accurate automated geolocating solutions. Herein, we present a new method to predict a Twitter user’s location based on the information in a single tweet. We integrate text and user profile meta-data into a single model using a convolutional neural network. Our experiments demonstrate that our neural model substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1% accuracy on city-level and country-level prediction respectively.

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Notes

  1. 1.

    https://dev.twitter.com/docs.

  2. 2.

    https://dev.twitter.com/streaming/reference/post/statuses/filter.

  3. 3.

    http://www.nltk.org/api/nltk.tokenize.html.

  4. 4.

    https://code.google.com/archive/p/word2vec/.

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Acknowledgments

This work was supported in part by the Office of Naval Research (ONR) N000140811186, and the National Science Foundation (NSF) 00361150115291. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the NSF. We want to thank tutors in the Global Communication Center at Carnegie Mellon for their valuable advice.

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, 5000 Forbe Ave., Pittsburgh, USA

    Binxuan Huang & Kathleen M. Carley

Authors
  1. Binxuan Huang
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  2. Kathleen M. Carley
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Corresponding author

Correspondence to Kathleen M. Carley .

Editor information

Editors and Affiliations

  1. Penn State University, State College, Pennsylvania, USA

    Dongwon Lee

  2. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Yu-Ru Lin

  3. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Nathaniel Osgood

  4. United States Military Academy, West Point, New York, USA

    Robert Thomson

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Huang, B., Carley, K.M. (2017). On Predicting Geolocation of Tweets Using Convolutional Neural Networks. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-60240-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60239-4

  • Online ISBN: 978-3-319-60240-0

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