Automating Linguistics-Based Cues for detecting deception in text-based asynchronous computer-mediated communication

Lina Zhou, Judee K. Burgoon, Jay F. Nunamaker, Doug Twitchell

Research output: Contribution to journalArticlepeer-review

292 Scopus citations


The detection of deception is a promising but challenging task. A systematic discussion of automated Linguistics Based Cues (LBC) to deception has rarely been touched before. The experiment studied the effectiveness of automated LBC in the context of text-based asynchronous computer mediated communication (TA-CMC). Twenty-seven cues either extracted from the prior research or created for this study were clustered into nine linguistics constructs: quantity, diversity, complexity, specificity, expressivity, informality, affect, uncertainty, and non-immediacy. A test of the selected LBC in a simulated TA-CMC experiment showed that: (1) a systematic analysis of linguistic information could be useful in the detection of deception; (2) some existing LBC were effective as expected, while some others turned out in the opposite direction to the prediction of the prior research; and (3) some newly discovered linguistic constructs and their component LBC were helpful in differentiating deception from truth.

Original languageEnglish (US)
Pages (from-to)81-106
Number of pages26
JournalGroup Decision and Negotiation
Issue number1
StatePublished - Jan 2004


  • Computer-mediated communication
  • Deception
  • Deception detection
  • Linguistics based cue
  • Natural language processing

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Arts and Humanities (miscellaneous)
  • Social Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation


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