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Novel Comment Spam Filtering Method on Youtube: Sentiment Analysis and Personality Recognition

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Current Trends in Web Engineering (ICWE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10544))

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Abstract

The deeply entrenched use of Online Social Networks (OSNs), where millions of users share unconsciously any kind of personal data, offers a very attractive channel to attackers. They provide the possibility of sending spam messages through different channels (wall posts, comments, private messages). In this paper we propose a novel spam filtering method focused on social media spam. It aims to demonstrate that using sentiment analysis and personality recognition techniques, in order to analyze the content of the texts, the improvement of spam filtering results is possible. We add these features to each OSN spam both independently and jointly, and then we compare Bayesian spam filters with and without the new features in terms of the number of false positive and accuracy. At the end, the results of the top ten filtering classifiers have been improved, reducing also the number of false positives (26.69% on average), reaching an 82.55% of accuracy.

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Notes

  1. 1.

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

  2. 2.

    https://www.youtube.com/yt/press/statistics.html.

  3. 3.

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

  4. 4.

    http://mlg.ucd.ie/yt/.

  5. 5.

    www.youtube.com.

  6. 6.

    http://www.cs.cornell.edu/People/pabo/movie-review-data/.

  7. 7.

    https://www.uclassify.com.

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Acknowledgments

This work has been developed by the intelligent systems for industrial systems group supported by the Department of Education, Language policy and Culture of the Basque Government. It has been partially funded by the Basque Department of Education, Language policy and Culture under the project SocialSPAM (PI 2014 1 102).

We thank Mattias Östmar for the valuable tools developed and published. And we thank Jon Kâgström (Founder of uClassify (https://www.uclassify.com)) for the opportunity to use their API for research purposes.

Author information

Authors and Affiliations

  1. Electronics and Computing Department, Mondragon University, Goiru Kalea, 2, 20500, Arrasate-Mondragón, Spain

    Enaitz Ezpeleta, Iñaki Garitano, Ignacio Arenaza-Nuño & Urko Zurutuza

  2. Pragsis Technologies, Manuel Tovar, 43-53, Fuencarral, 28034, Madrid, Spain

    José María Gómez Hidalgo

Authors
  1. Enaitz Ezpeleta
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  2. Iñaki Garitano
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  3. Ignacio Arenaza-Nuño
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  4. José María Gómez Hidalgo
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  5. Urko Zurutuza
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Corresponding author

Correspondence to Enaitz Ezpeleta .

Editor information

Editors and Affiliations

  1. Universidad de Alicante, Alicante, Spain

    Irene Garrigós

  2. Institute of Software Technology and Interactive Systems, TU Wien, Vienna, Austria

    Manuel Wimmer

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Ezpeleta, E., Garitano, I., Arenaza-Nuño, I., Hidalgo, J.M.G., Zurutuza, U. (2018). Novel Comment Spam Filtering Method on Youtube: Sentiment Analysis and Personality Recognition. In: Garrigós, I., Wimmer, M. (eds) Current Trends in Web Engineering. ICWE 2017. Lecture Notes in Computer Science(), vol 10544. Springer, Cham. https://doi.org/10.1007/978-3-319-74433-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-74433-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74432-2

  • Online ISBN: 978-3-319-74433-9

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