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A Mood Analysis on Youtube Comments and a Method for Improved Social Spam Detection

  • Enaitz Ezpeleta
  • Mikel Iturbe
  • Iñaki Garitano
  • Iñaki Velez de Mendizabal
  • Urko Zurutuza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10870)

Abstract

In the same manner that Online Social Networks (OSN) usage increases, non-legitimate campaigns over these types of web services are growing. This is the reason why significant number of users are affected by social spam every day and therefore, their privacy is threatened. To deal with this issue in this study we focus on mood analysis, among all content-based analysis techniques. We demonstrate that using this technique social spam filtering results are improved. First, the best spam filtering classifiers are identified using a labeled dataset consisting of Youtube comments, including spam. Then, a new dataset is created adding the mood feature to each comment, and the best classifiers are applied to it. A comparison between obtained results with and without mood information shows that this feature can help to improve social spam filtering results: the best accuracy is improved in two different datasets, and the number of false positives is reduced 13.76% and 11.41% on average. Moreover, the results are validated carrying out the same experiment but using a different dataset.

Keywords

Spam Social spam Mood analysis Online Social Networks Youtube 

Notes

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. This work was partially supported by the project Semantic Knowledge Integration for Content-Based Spam Filtering (TIN2017-84658-C2-2-R) from the Spanish Ministry of Economy, Industry and Competitiveness (SMEIC), State Research Agency (SRA) and the European Regional Development Fund (ERDF).

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.

Iñaki Garitano is partially supported by the INCIBE grant “INCIBEC-2015-02495” corresponding to the “Ayudas para la Excelencia de los Equipos de Investigación avanzada en ciberseguridad”.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Enaitz Ezpeleta
    • 1
  • Mikel Iturbe
    • 1
  • Iñaki Garitano
    • 1
  • Iñaki Velez de Mendizabal
    • 1
  • Urko Zurutuza
    • 1
  1. 1.Electronics and Computing DepartmentMondragon UniversityArrasate-MondragónSpain

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