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Effects of the Inclusion of Non-newsworthy Messages in Credibility Assessment

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Advances in Computational Intelligence (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10061))

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Abstract

Social media has become influential and affects large public perception. Anyone can post and share messages on social networking sites. However, not all posts are trustworthy. Many online messages contain misleading or false information. There has been an extensive research to assess the credibility of social media data. Previous studies evaluate all online messages, which may be inappropriate due to a large amount of such data that can result in ineffectiveness of the system. This paper studies and presents the effects of the inclusion of such data—namely, non-newsworthy messages—in credibility assessment. Our findings affirm a negative effect of training a model with non-newsworthy data. The degree of performance degradation is also shown to have a strong connection to a degree of non-newsworthiness in training data.

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Notes

  1. 1.

    https://www.facebook.com/.

  2. 2.

    https://www.instagram.com/?hl=en or https://www.instagram.com/?hl=th.

  3. 3.

    https://twitter.com/?lang=en or https://twitter.com/?lang=th.

  4. 4.

    http://newsroom.fb.com/company-info/.

  5. 5.

    https://www.instagram.com/press/?hl=en.

  6. 6.

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

  7. 7.

    http://www.phishtank.com/.

  8. 8.

    https://developers.google.com/safebrowsing/.

  9. 9.

    https://pypi.python.org/pypi/scikit-learn/0.16.0.

  10. 10.

    https://github.com/chaluemwut/FBFilterCML.git.

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Acknowledgements

This project is partially supported by Natural Sciences and Engineering Research Council of Canada (NSERC).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand

    Chaluemwut Noyunsan, Tatpong Katanyukul & Kanda Runapongsa Saikaew

  2. Department of Computer Science, Faculty of Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada

    Carson K. Leung

Authors
  1. Chaluemwut Noyunsan
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  2. Tatpong Katanyukul
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  3. Carson K. Leung
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  4. Kanda Runapongsa Saikaew
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Corresponding author

Correspondence to Tatpong Katanyukul .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Universidad Autónoma Metropolitana, Mexico City, Mexico

    Oscar Herrera-Alcántara

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Noyunsan, C., Katanyukul, T., Leung, C.K., Saikaew, K.R. (2017). Effects of the Inclusion of Non-newsworthy Messages in Credibility Assessment. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-62434-1_15

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  • Print ISBN: 978-3-319-62433-4

  • Online ISBN: 978-3-319-62434-1

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