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International Conference on Asian Digital Libraries

ICADL 2017: Digital Libraries: Data, Information, and Knowledge for Digital Lives pp 267–278Cite as

Automatic Discovery of Abusive Thai Language Usages in Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10647))

Abstract

Social networks have become a standard means of communication that allows a massive amount of users to interact and consume information anywhere and anytime. In Thailand, millions of users have access to social networks, a majority of which include young children. The colloquial nature of social media inherently encourages certain expressions of language that do not conform to the standard, some of which may be considered abusive and offensive. Such ill-mannered language fashion has become increasingly used by a large number of Thai social media users. If these abusive languages are exposed to adolescents without proper guidance, they could compulsorily develop a familiar attitude towards such language styles. To address the issue, we present a set of algorithms based on machine learning, that automatically detect abusive Thai language in social networks. Our best results yield 86% f-measure (88.73% precision and 83.53% recall).

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Notes

  1. 1.

    https://www.statista.com/statistics/490467/number-of-thailand-facebook-users/.

  2. 2.

    http://www.csie.ntu.edu.tw/cjlin/libsvm/.

  3. 3.

    http://www.cs.waikato.ac.nz/ml/weka/.

  4. 4.

    https://www.facebook.com/.

  5. 5.

    https://developers.facebook.com/docs/graph-api.

  6. 6.

    https://www.facebook.com/ejeab/.

  7. 7.

    https://www.facebook.com/nongngneverdie/.

  8. 8.

    https://www.facebook.com/sudlokomteen/.

  9. 9.

    https://github.com/wittawatj/ctwt.

  10. 10.

    http://thailang.nectec.or.th/best/?q=node/21.

  11. 11.

    http://www.sansarn.com/lexto/.

  12. 12.

    https://docs.oracle.com/javase/7/docs/api/java/text/BreakIterator.html.

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Acknowledgment

This research project was partially supported by Faculty of Information and Communication Technology, Mahidol University.

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Authors and Affiliations

  1. Faculty of Information and Communication Technology, Mahidol University, Salaya, Thailand

    Suppawong Tuarob & Jarernsri L. Mitrpanont

Authors
  1. Suppawong Tuarob
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  2. Jarernsri L. Mitrpanont
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Corresponding author

Correspondence to Suppawong Tuarob .

Editor information

Editors and Affiliations

  1. Chulalongkorn University, Bangkok, Thailand

    Songphan Choemprayong

  2. University of Lugano, Lugano, Switzerland

    Fabio Crestani

  3. Waikato University, Hamilton, New Zealand

    Sally Jo Cunningham

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Tuarob, S., Mitrpanont, J.L. (2017). Automatic Discovery of Abusive Thai Language Usages in Social Networks. In: Choemprayong, S., Crestani, F., Cunningham, S. (eds) Digital Libraries: Data, Information, and Knowledge for Digital Lives. ICADL 2017. Lecture Notes in Computer Science(), vol 10647. Springer, Cham. https://doi.org/10.1007/978-3-319-70232-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-70232-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70231-5

  • Online ISBN: 978-3-319-70232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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