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Detecting Deception Through Linguistic Analysis

Detecting Deception Through Linguistic Analysis. Judee K. Burgoon J. P. Blair Tiantian Qin Jay F. Nunamaker, Jr. Introduction. Intelligence analysts are required to sift through mountains of information Humans are generally bad at detecting deception

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Detecting Deception Through Linguistic Analysis

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  1. Detecting Deception Through Linguistic Analysis Judee K. Burgoon J. P. Blair Tiantian Qin Jay F. Nunamaker, Jr.

  2. Introduction • Intelligence analysts are required to sift through mountains of information • Humans are generally bad at detecting deception • Mounting evidence suggests that CMC makes humans even less accurate at detecting deception • There is a need to develop tools to help humans detect deception

  3. Background • Desert Survival • 2 (Deception – Truthful) X 2 (FtF or CMC) • All discussions were transcribed • Linguistic Analysis conducted on 27 possible indicators of deception • Mock Theft Pilot • 2 (Deception – Truthful) X 2 (FtF or Text) • Transcribed and Analyzed

  4. Hypotheses • Deceivers will display higher • Quantity • Expressiveness

  5. Hypotheses • Deceivers will display less • Complexity • Vocabulary • Grammatical

  6. Method • Students recruited from a multi-sectioned communications class • Half were randomly assigned to be thieves and half to be innocents • Thieves “stole” a wallet that was left in their classroom • Innocents were told that a “theft” would occur on a given day

  7. Method • Subjects were motivated to do well by telling them that they could earn $10 if they convinced the interviewer that they were innocent • An additional $50 was to be awarded to the person who was the most successful at convincing the interviewer.

  8. Method • Participants were interviewed by trained interviewers using a standardized BAI format in one of three modalities • FtF • Text/Chat • Audio Conference • All interviews were recorded and transcribed

  9. Method • Analysis was conducted using shallow parsers (Grok and Iskim) or look-up dictionaries • Classes of Cues • Quantity (Words, Syllables, Sentences) • Vocabulary Complexity (Big words and Syllables per word) • Grammatical Complexity (Short and Long Sentences, Flesch-Kincaid, and others) • Expressiveness (Rate of Adj and Adv, emotiveness index, affective terms)

  10. MOCK THEFT RESULTS Decision Tree Analysis Note: Sample Tree from text modality with no duplicated cues

  11. MOCK THEFT RESULTS Decision Tree Analysis Note: Sample tree from text modality, significant cues only

  12. 78.58% 75% 62.5% 60.42% 58.33% Original Significant Only txt;Significant No Duplicate Only txt; Noduplicate DECISION TREE IMPROVEMENT

  13. Conclusions • We were able to identify some linguistic indicators of deception • Modality also appears to affect several indicators • These indicators could be subjected to a pruned tree algorithm to classify subjects as truthful or deceptive • Future research will serve to further improve modeling

  14. Future Research • The linguistic model will be improved by • Adding more data • Improving dictionaries • Focusing on different models for different communication contexts • Adding subjective operator evaluations

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