An investigation of data and text mining methods for real world deception detection

Christie M. Fuller, David P. Biros, Dursun Delen

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

Uncovering lies (or deception) is of critical importance to many including law enforcement and security personnel. Though these people may try to use many different tactics to discover deception, previous research tells us that this cannot be accomplished successfully without aid. This manuscript reports on the promising results of a research study where data and text mining methods along with a sample of real-world data from a high-stakes situation is used to detect deception. At the end, the information fusion based classification models produced better than 74% classification accuracy on the holdout sample using a 10-fold cross validation methodology. Nonetheless, artificial neural networks and decision trees produced accuracy rates of 73.46% and 71.60% respectively. However, due to the high stakes associated with these types of decisions, the extra effort of combining the models to achieve higher accuracy is well warranted.

Original languageEnglish
Pages (from-to)8392-8398
Number of pages7
JournalExpert Systems with Applications
Volume38
Issue number7
DOIs
StatePublished - 1 Jul 2011
Externally publishedYes

Keywords

  • Classification
  • Credibility assessment
  • Data mining
  • Deception detection
  • Information fusion
  • Text mining

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