The Association of Digital Forensics, Security and Law (ADFSL)
Linguistic deception theory provides methods to discover potentially deceptive texts to make them accessible to clerical review. This paper proposes the integration of these linguistic methods with traditional e-discovery techniques to identify deceptive texts within a given author’s larger body of written work, such as their sent email box. First, a set of linguistic features associated with deception are identified and a prototype classifier is constructed to analyze texts and describe the features’ distributions, while avoiding topic-specific features to improve recall of relevant documents. The tool is then applied to a portion of the Enron Email Dataset to illustrate how these strategies identify records, providing an example of its advantages and capability to stratify the large data set at hand.
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Crabb, Erin S.
"“Time for Some Traffic Problems”: Enhancing E-Discovery and Big Data Processing Tools with Linguistic Methods for Deception Detection,"
Journal of Digital Forensics, Security and Law: Vol. 9
, Article 14.
Available at: http://commons.erau.edu/jdfsl/vol9/iss2/14