Plagiarism Detection Based on Citing Sentences

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10450)

Abstract

Plagiarism, which is one of the forms of academic misconducts, is problematic. It results in discouraging innovation, and losing trust in the academic community. We modeled the plagiarism for academic publications, by means of the similarity between textual contents, and citation relations. Furthermore, we adopted the model in our proposed method for plagiarism detection. We evaluate our method using two types of dataset, namely auto-simulated and manually judged dataset. Our experiment shows that our method outperforms the baseline, which only uses the similarity between textual contents, on the auto-simulated dataset and the manually judged one for the ACL sub-dataset.

Keywords

Plagiarism detection Information retrieval Citation analysis 

References

  1. 1.
    Fang, F.C., Steen, R.G., Casadevall, A.: Misconduct accounts for the majority of retracted scientific publications. Proc. Nat. Acad. Sci. 109(42), 17028–17033 (2012). doi:10.1073/pnas.1212247109. NASCrossRefGoogle Scholar
  2. 2.
    Kessler, M.M.: Bibliographic coupling between scientific papers. Am. Documentation 14(1), 10–25 (1963). doi:10.1002/asi.5090140103. WileyCrossRefGoogle Scholar
  3. 3.
    Potthast, M., Gollub, T., Hagen, M., Graßegger, J., Kiesel, J., Michel, M., Oberländer, A., Tippmann, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., Stein, B.: Overview of the 4th international competition on plagiarism detection. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) Working Notes Papers of the CLEF 2012 Evaluation Labs (2012)Google Scholar
  4. 4.
    Gupta, P., Rosso, P.: Text reuse with ACL: (upward) trends. In: Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries, pp. 76–82. ACL (2012)Google Scholar
  5. 5.
    Alzahrani, S., Palade, V., Salim, N., Abraham, A.: Using structural information and citation evidence to detect significant plagiarism cases in scientific publications. J. Am. Soc. Inf. Sci. 63(2), 286–312 (2012). doi:10.1002/asi.21651. WileyCrossRefGoogle Scholar
  6. 6.
    HaCohen-Kerner, Y., Tayeb, A., Ben-Dror, N.: Detection of simple plagiarism in computer science papers. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 421–429. ACL (2010)Google Scholar
  7. 7.
    Gipp, B., Meuschke, N.: Citation pattern matching algorithms for citation-based plagiarism detection: greedy citation tiling, citation chunking and longest common citation sequence. In: Proceedings of the 11th ACM Symposium on Document Engineering, pp. 249–258. ACM (2011). doi:10.1145/2034691.2034741
  8. 8.
    Pertile, S.D.L., Moreira, V.P., Rosso, P.: Comparing and combining content-and citation-based approaches for plagiarism detection. J. Assn. Inf. Sci. Tec. 67(10), 2511–2526 (2016). doi:10.1002/asi.23593. WileyCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyTokyoJapan

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