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Using Named Entities for Computer-Automated Verbal Deception Detection.

Bennett Kleinberg1, Maximilian Mozes1,2, Arnoud Arntz1

  • 1Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129 D, 1018 WS, Amsterdam, The Netherlands.

Journal of Forensic Sciences
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Summary
This summary is machine-generated.

Automated deception detection can be improved using named entity recognition (NER). NER identifies details in truthful accounts and outperforms other methods for detecting deception in hotel reviews.

Keywords:
computational linguisticscriteria-based content analysisdeception detectionforensic sciencelinguistic inquiry and word countnamed entity recognitionreality monitoring

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Deception Detection

Background:

  • Growing need for automated systems to detect verbal deception.
  • Existing methods may not fully capture linguistic nuances of truthfulness.
  • Theoretical principles suggest truth-tellers provide more detailed and contextual information.

Purpose of the Study:

  • To investigate the utility of named entity recognition (NER) for automated verbal deception detection.
  • To evaluate if NER can model theoretical principles of truthful versus deceptive accounts.
  • To assess NER's effectiveness in distinguishing truthful from deceptive hotel reviews.

Main Methods:

  • Applied two Named Entity Recognition (NER) tools (spaCy, Stanford NER) to extract named entities from hotel reviews.
  • Quantified the proportion of named entities as a measure of detail and contextual references.
  • Compared NER's discriminative performance against LIWC (lexicon word count) and speciteller (sentence specificity).

Main Results:

  • Named entities significantly discriminated between truthful and deceptive hotel reviews above chance levels.
  • NER-based features demonstrated superior performance compared to LIWC and speciteller.
  • The proportion of named entities effectively captured theoretical concepts related to deception.

Conclusions:

  • Named entity recognition is a viable and effective method for automated verbal deception detection.
  • NER offers a valuable addition to existing approaches by capturing detailed and contextual information.
  • This study supports the use of NER for enhancing the accuracy of deception detection systems.