Plagiarism Detection in arXiv

Hong Kong December 18-December 22

DOI Bookmark:

http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.126

Sixth IEEE International Conference o ...

	This Article
	PURCHASE ARTICLE: $19 PDF HTML IEEE Xplore Subscribers
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan/Endnote
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Daria Sorokina Articles by Johannes Gehrke Articles by Simeon Warner Articles by Paul Ginsparg

	ASCII Text	x
	Daria Sorokina, Johannes Gehrke, Simeon Warner, Paul Ginsparg, "Plagiarism Detection in arXiv," Data Mining, IEEE International Conference on, pp. 1070-1075, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006.

	BibTex	x
	@article{ 10.1109/ICDM.2006.126, author = {Daria Sorokina and Johannes Gehrke and Simeon Warner and Paul Ginsparg}, title = {Plagiarism Detection in arXiv}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2006}, issn = {1550-4786}, pages = {1070-1075}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.126}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Plagiarism Detection in arXiv SN - 1550-4786 SP1070 EP1075 A1 - Daria Sorokina, A1 - Johannes Gehrke, A1 - Simeon Warner, A1 - Paul Ginsparg, PY - 2006 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Daria Sorokina, Cornell University, USA

Johannes Gehrke, Cornell University, USA

Simeon Warner, Cornell University, USA

Paul Ginsparg, Cornell University, USA

We describe a large-scale application of methods for finding plagiarism in research document collections. The methods are applied to a collection of 284,834 documents collected by arXiv.org over a 14 year period, covering a few different research disciplines. The methodology effi- ciently detects a variety of problematic author behaviors, and heuristics are developed to reduce the number of false positives. The methods are also efficient enough to imple- ment as a real-time submission screen for a collection many times larger.

Citation:

Daria Sorokina, Johannes Gehrke, Simeon Warner, Paul Ginsparg, "Plagiarism Detection in arXiv," icdm, pp.1070-1075, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.