Can Twitter Be an Alternative of Real-World Sensors?

  • Tetsuro Takahashi
  • Shuya Abe
  • Nobuyuki Igata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6763)


Twitter is the most famous on-line microblogging service now. People can post (tweet) what they are doing in 140 characters. Since Twitter posts (tweets) reflect what people are looking, hearing, feeling and so on, we can obtain information about Real-world phenomena through the large amount of tweets. In other words, Twitter can be regarded as a sensor of Real-world phenomena including natural phenomena such as hay fever. This motivated us to investigate whether can Twitter be an alternative of Real-world Sensor. In this paper, we first describe about our system which collects and analyzes tweets in order to generates a hay fever map just like as a weather report map. There are some difficulties such as location estimation and normalization of number of tweets. Using the output of the system, we discuss the comparison with actual pollen data gathered by real sensors. The result shows that Twitter can reflect natural phenomena in some particular areas.


Twitter crowd knowledge social sensor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval A1 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  2. 2.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web mining and Social Network Analysis, WebKDD/SNA-KDD 2007, pp. 56–65. ACM, New York (2007)Google Scholar
  3. 3.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on World Wide Web, WWW 2010, pp. 591–600. ACM, New York (2010)Google Scholar
  4. 4.
    Naaman, M., Boase, J., Lai, C.H.: Is it really about me?: message content in social awareness streams. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, CSCW 2010, pp. 189–192. ACM, New York (2010)CrossRefGoogle Scholar
  5. 5.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM, New York (2010)Google Scholar
  6. 6.
    Iwakura, T., Okamoto, S.: A fast boosting-based learner for feature-rich tagging and chunking. In: Proceedings of the Twelfth Conference on Computational Natural Language Learning, CoNLL 2008, pp. 17–24. Association for Computational Linguistics, Morristown (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tetsuro Takahashi
    • 1
  • Shuya Abe
    • 1
  • Nobuyuki Igata
    • 1
  1. 1.Fujitsu Laboratories, Ltd.KawasakiJapan

Personalised recommendations