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Tweet Location Inference Based on Contents and Temporal Association

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Book cover Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9419))

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Abstract

How can we infer a tweet location? Are timestamps of tweets effective for the location inference? In this study, we propose a novel method for tweet location inference based on contents and timestamps of tweets. It is important to infer the locations of tweets for the services related to locations such as recommending restaurants, sending disaster-related information to users, and providing commercial messages to users. This study has two contributions: (1) we propose a novel method to infer tweet locations based on the contents and timestamps of tweets, and (2) we experimentally demonstrate the effectiveness of the proposed method using Twitter data. The experimental results suggest that the proposed method can infer tweet locations more precisely than a baseline that does not take the temporal association into account.

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Notes

  1. 1.

    https://twitter.com/.

  2. 2.

    Tweets posted from Japan.

  3. 3.

    http://dev.twitter.com/streaming/overview.

  4. 4.

    http://taku910.github.io/mecab/.

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Acknowledgment

This research was partly supported by the program “Research and Development on Real World Big Data Integration and Analysis” of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Correspondence to Saki Ueda .

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© 2015 Springer International Publishing Switzerland

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Ueda, S., Yamaguchi, Y., Kitagawa, H., Amagasa, T. (2015). Tweet Location Inference Based on Contents and Temporal Association. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-26187-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26186-7

  • Online ISBN: 978-3-319-26187-4

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