Cross Language Prediction of Vandalism on Wikipedia Using Article Views and Revisions

  • Khoi-Nguyen Tran
  • Peter Christen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

Abstract

Vandalism is a major issue on Wikipedia, accounting for about 2% (350,000+) of edits in the first 5 months of 2012. The majority of vandalism are caused by humans, who can leave traces of their malicious behaviour through access and edit logs. We propose detecting vandalism using a range of classifiers in a monolingual setting, and evaluated their performance when using them across languages on two data sets: the relatively unexplored hourly count of views of each Wikipedia article, and the commonly used edit history of articles. Within the same language (English and German), these classifiers achieve up to 87% precision, 87% recall, and F1-score of 87%. Applying these classifiers across languages achieve similarly high results of up to 83% precision, recall, and F1-score. These results show characteristic vandal traits can be learned from view and edit patterns, and models built in one language can be applied to other languages.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Priedhorsky, R., Chen, J., Lam, S.T.K., Panciera, K., Terveen, L., Riedl, J.: Creating, destroying, and restoring value in wikipedia. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work, GROUP 2007, pp. 259–268. ACM, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Viégas, F.B., Wattenberg, M., Dave, K.: Studying cooperation and conflict between authors with history flow visualizations. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2004, pp. 575–582. ACM, New York (2004)CrossRefGoogle Scholar
  3. 3.
    Kittur, A., Suh, B., Pendleton, B.A., Chi, E.H.: He says, she says: conflict and coordination in wikipedia. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2007, pp. 453–462. ACM, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Smets, K., Goethals, B., Verdonk, B.: Automatic vandalism detection in wikipedia: Towards a machine learning approach. In: AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 43–48 (2008)Google Scholar
  5. 5.
    Panciera, K., Halfaker, A., Terveen, L.: Wikipedians are born, not made: a study of power editors on wikipedia. In: Proceedings of the ACM 2009 International Conference on Supporting Group Work, GROUP 2009, pp. 51–60. ACM, New York (2009)CrossRefGoogle Scholar
  6. 6.
    Potthast, M., Stein, B., Gerling, R.: Automatic vandalism detection in wikipedia. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 663–668. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Rzeszotarski, J., Kittur, A.: Learning from history: predicting reverted work at the word level in wikipedia. In: Proc. of the ACM 2012 Conf. on Computer Supported Cooperative Work, CSCW 2012, pp. 437–440. ACM, New York (2012)CrossRefGoogle Scholar
  8. 8.
    Chin, S.C., Street, W.N., Srinivasan, P., Eichmann, D.: Detecting wikipedia vandalism with active learning and statistical language models. In: Proc. of the 4th Workshop on Information Credibility, WICOW 2010, pp. 3–10. ACM (2010)Google Scholar
  9. 9.
    Wang, W.Y., McKeown, K.: ”got you!”: Automatic vandalism detection in wikipedia with web-based shallow syntactic-semantic modeling. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Beijing, China. Coling 2010 Organizing Committee, pp. 1146–1154 (August 2010)Google Scholar
  10. 10.
    Harpalani, M., Hart, M., Singh, S., Johnson, R., Choi, Y.: Language of vandalism: Improving wikipedia vandalism detection via stylometric analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2, pp. 83–88 (2011)Google Scholar
  11. 11.
    Adler, B., de Alfaro, L., Pye, I.: Detecting wikipedia vandalism using wikitrust. Notebook Papers of CLEF 1, 22–23 (2010)Google Scholar
  12. 12.
    West, A.G., Kannan, S., Lee, I.: Detecting wikipedia vandalism via spatio-temporal analysis of revision metadata? In: Proceedings of the Third European Workshop on System Security, EUROSEC 2010, pp. 22–28. ACM, New York (2010)CrossRefGoogle Scholar
  13. 13.
    Potthast, M.: Crowdsourcing a wikipedia vandalism corpus. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 789–790. ACM, New York (2010)Google Scholar
  14. 14.
    Potthast, M., Holfeld, T.: Overview of the 2nd international competition on wikipedia vandalism detection. In: Notebook for PAN at CLEF (2011)Google Scholar
  15. 15.
    Velasco, S.: Wikipedia vandalism detection through machine learning: Feature review and new proposals. In: Lab Report for PAN-CLEF 2010 (2010)Google Scholar
  16. 16.
    West, A.G., Lee, I.: Multilingual vandalism detection using language-independent & ex post facto evidence - notebook for pan at clef 2011. In: Petras, V., Forner, P., Clough, P.D. (eds.) CLEF (Notebook Papers/Labs/Workshop) (2011)Google Scholar
  17. 17.
    Wu, Q., Irani, D., Pu, C., Ramaswamy, L.: Elusive vandalism detection in wikipedia: a text stability-based approach. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1797–1800. ACM, New York (2010)Google Scholar
  18. 18.
    Laurent, M., Vickers, T.: Seeking health information online: does wikipedia matter? Journal of the American Medical Informatics Association 16(4), 471–479 (2009)CrossRefGoogle Scholar
  19. 19.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)Google Scholar
  20. 20.
    Rigutini, L., Maggini, M., Liu, B.: An em based training algorithm for cross-language text categorization. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 529–535 (September 2005)Google Scholar
  21. 21.
    Liu, Y., Dai, L., Zhou, W., Huang, H.: Active learning for cross language text categorization. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part I. LNCS, vol. 7301, pp. 195–206. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Potthast, M., Stein, B., Holfeld, T.: Overview of the 1st international competition on wikipedia vandalism detection. In: Braschler, M., Harman, D., Pianta, E. (eds.) CLEF (Notebook Papers/LABs/Workshops) (2010)Google Scholar
  23. 23.
    White, J., Maessen, R.: Zot! to wikipedia vandalism - lab report for pan at clef 2010. In: CLEF (Notebook Papers/LABs/Workshops) (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Khoi-Nguyen Tran
    • 1
  • Peter Christen
    • 1
  1. 1.Research School of Computer ScienceThe Australian National UniversityCanberraAustralia

Personalised recommendations