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Classifying Twitter Users Based on User Profile and Followers Distribution

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Database and Expert Systems Applications (DEXA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8055))

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Abstract

We propose methods to classify Twitter users into open accounts and closed accounts. Open accounts (shop accounts, etc.) are the accounts who publish information to general public and their intentions is to promotion products, services or themselves. On the other hand, closed accounts tweet information on their daily lives or use Twitter as a communication tool with their friends. To distinguish these two different kinds of Twitter users can help us to search for local and daily information on Twitter. We classify Twitter accounts based on user profiles and followers distributions. The features of profile of open accounts include clue keywords, telephone number, detailed address, and so on. Follower distribution is another notable feature: most open accounts have followers from variety community. The experimental results validate our methods.

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References

  1. Yan, L., Ma, Q., Yoshikawa, M.: Where can I Buy iPhone4S Now?: Spatio-Temporal Entity Retrieval on Twitter. DEIM Forum 2012 (2012)

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  2. Yan, L., Ma, Q., Yoshikawa, M.: Classifying Twitter Users for Spatio-temporal Entity Retrieval. IPSJ Technical Reports 2012-DBS-156(15), 1–6 (2012)

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  3. Pennacchiotti, M., Popescu, A.: A Machine Learning Approach to Twitter User Classification. In: ICWSM 2011, pp. 281–288 (2011)

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  4. Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: Understanding Microblogging Usage and Communities. In: SNA-KDD 2007, pp. 56–65 (2007)

    Google Scholar 

  5. Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Who is Tweeting on Twitter: Human, Bot, or Cyborg? In: ACSAC 2010, pp. 21–30 (2010)

    Google Scholar 

  6. Bastian, M., Heymann, S., Jacomy, M.: Gephi: An Open Source Software for Exploring and Manipulating Networks. In: ICWSM 2009, pp. 361–362 (2009)

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© 2013 Springer-Verlag Berlin Heidelberg

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Yan, L., Ma, Q., Yoshikawa, M. (2013). Classifying Twitter Users Based on User Profile and Followers Distribution. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_34

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  • DOI: https://doi.org/10.1007/978-3-642-40285-2_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40284-5

  • Online ISBN: 978-3-642-40285-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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