A comparison of classification methods for predicting deception in computer-mediated communication

Lina Zhou, Judee K. Burgoon, Douglas P. Twitchell, Tiantian Qin, Jay F. Nunamaker

Research output: Contribution to journalArticle

152 Scopus citations

Abstract

The increased chance of deception in computer-mediated communication and the potential risk of taking action based on deceptive information calls for automatic detection of deception. To achieve the ultimate goal of automatic prediction of deception, we selected four common classification methods and empirically compared their performance in predicting deception. The deception and truth data were collected during two experimental studies. The results suggest that all of the four methods were promising for predicting deception with cues to deception. Among them, neural networks exhibited consistent performance and were robust across test settings. The comparisons also highlighted the importance of selecting important input variables and removing noise in an attempt to enhance the performance of classification methods. The selected cues offer both methodological and theoretical contributions to the body of deception and information systems research.

Original languageEnglish (US)
Pages (from-to)139-166
Number of pages28
JournalJournal of Management Information Systems
Volume20
Issue number4
StatePublished - Jan 1 2004

Keywords

  • Classification methods
  • Deception
  • Deception detection
  • Linguistic cues

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management

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