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Precedent-Based Approach for the Identification of Deviant Behavior in Social Media

  • Anna V. Kalyuzhnaya
  • Nikolay O. Nikitin
  • Nikolay Butakov
  • Denis Nasonov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)

Abstract

The current paper is devoted to a problem of deviant users’ identification in social media. For this purpose, each user of social media source should be described through a profile that aggregates open information about him/her within the special structure. Aggregated user profiles are formally described in terms of multivariate random process. The special emphasis in the paper is made on methods for identifying of users with certain on a base of few precedents and control the quality of search results. Experimental study shows the implementation of described methods for the case of commercial usage of the personal account in social media.

Keywords

Deviant user Social media Behavior pattern Precedent-based search 

Notes

Acknowledgments

This research financially supported by Ministry of Education and Science of the Russian Federation, Agreement #14.578.21.0196 (03.10.2016). Unique Identification RFMEFI57816X0196.

References

  1. 1.
    Raisi, E., Huang, B.: Cyberbullying identification using participant-vocabulary consistency, pp. 46–50 (2016)Google Scholar
  2. 2.
    Sax, S.: Flame Wars: Automatic Insult DetectionGoogle Scholar
  3. 3.
    Wang, Y., et al.: Topic-level influencers identification in the Microblog sphere, pp. 4–5 (2016)Google Scholar
  4. 4.
    Angeletou, S., Rowe, M., Alani, H.: Modelling and analysis of user behaviour in online communities. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 35–50. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25073-6_3CrossRefGoogle Scholar
  5. 5.
    Dadvar, M., Ordelman, R., de Jong, F., Trieschnigg, D.: Towards user modelling in the combat against cyberbullying. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds.) NLDB 2012. LNCS, vol. 7337, pp. 277–283. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31178-9_34CrossRefGoogle Scholar
  6. 6.
    Galal, A., Elkorany, A.: Dynamic modeling of twitter users dynamic modeling of twitter users. In: Proceedings if the 17th International Conference on Enterprise Information Systems, vol. 2, pp. 585–593 (2015)Google Scholar
  7. 7.
    Wolters, M.A.: A genetic algorithm for selection of fixed-size subsets with application to design problems. J. Stat. Softw. 68(1), 1–18 (2015)MathSciNetGoogle Scholar
  8. 8.
    Ma, Q., et al.: A sub-linear, massive-scale look-alike audience extension system. In: Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (2016)Google Scholar
  9. 9.
    Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213–220. ACM (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anna V. Kalyuzhnaya
    • 1
  • Nikolay O. Nikitin
    • 1
  • Nikolay Butakov
    • 1
  • Denis Nasonov
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussian Federation

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