Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach

The Verifiability Approach (VA) is a promising new approach for deception detection. It extends existing verbal credibility assessment tools by asking interviewees to provide statements rich in verifiable detail. Details that i) have been experienced with an identifiable person, ii) have been witnessed by an identifiable person, or iii) have been recorded through technology, are labelled as verifiable. With only minimal modifications of information-gathering interviews this approach has yielded remarkable classification accuracies. Currently, the VA relies on extensive manual annotation by human coders. Aiming to extend the VA’s applicability, we present a work in progress on automated VA scoring. We provide a conceptual outline of two automation approaches: one being based on the Linguistic Inquiry and Word Count software and the other on rule-based shallow parsing and named entity recognition. Differences between both approaches and possible future steps for an automated VA are discussed.

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