A theoretical basis to the automated detection of copying between texts, and its practical implementation in the Ferret plagiarism and collusion detector

Fundamental features of natural language can be exploited to produce an effective system for the--automated detection of plagiarism and collusion. Independently written texts can be effectively identified--as they have markedly different characteristics to those that include passages that have been fully or--partially copied. This paper describes the implementation of the Ferret plagiarism and collusion detector, and its use in the University of Hertfordshire and other institutions. The difference between human and machine analysis is examined, and we conclude that an approach using machine processing is likely to be necessary in many situations.