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Sense and Focus: Towards Effective Location Inference and Event Detection on Twitter

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

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Abstract

Twitter users post observations about their immediate environment as a part of the 500 million tweets posted everyday. As such, Twitter can become the source for invaluable information about objects, locations, and events, which can be analyzed and monitored in real time, not only to understand what is happening in the world, but also an event’s exact location. However, Twitter data is noisy as sensory values, and information such as the location of a tweet may not be available, e.g., only 0.9 % of tweets have GPS data. Due to the lack of accurate and fine-grained location information, existing Twitter event monitoring systems focus on city-level or coarser location identification, which cannot provide details for local events. In this paper, we propose SNAF (Sense and Focus), an event monitoring system for Twitter data that emphasizes local events. We increase the availability of the location information significantly by finding locations in tweet messages and users’ past tweets. We apply data cleaning techniques in our system, and with extensive experiments, we show that our method can improve the accuracy of location inference by 5 % to 20 % across different error ranges. We also show that our prototype implementation of SNAF can identify critical local events in real time, in many cases earlier than news reports.

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Notes

  1. 1.

    https://about.twitter.com/company.

  2. 2.

    http://wiki.dbpedia.org/Datasets.

  3. 3.

    https://dev.twitter.com/rest/public/timelines.

  4. 4.

    https://dev.twitter.com/streaming/public.

  5. 5.

    http://news.google.com.

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Authors and Affiliations

  1. School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia

    Yihong Zhang, Claudia Szabo & Quan Z. Sheng

Authors
  1. Yihong Zhang
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  2. Claudia Szabo
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  3. Quan Z. Sheng
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Corresponding author

Correspondence to Yihong Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2015 Springer International Publishing Switzerland

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Zhang, Y., Szabo, C., Sheng, Q.Z. (2015). Sense and Focus: Towards Effective Location Inference and Event Detection on Twitter. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_31

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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