Neuro-Linguistic Programming as a Verbal Lie Detection Tool: A Case Study of Pakistani Digital Media Programs

Abstract

This research sightsees the application of neurolinguistic programming techniques by Pakistani fact-finding journalists with the accused and spectators in digital media programs. Besides, it extracts trickery indicators along with the frequencies. It also answers how the model is operative for the neurolinguistic lie detection system. These objectives are accomplished through a mixed-methods research paradigm. The data are collected from secondary sources, in which the accused, spectators, and inspecting journalists’ turn-taking utterances are nominated for linguistic examination. For the case study, 2 case studies are taken from discussions in accountability reporting through purposive and convenient sampling. The researchers took 2 male and 2 female accused’s spoken data. After that, the AI-powered ‘TurboScribe’ tool and ‘Google Documents’ are used for audio-to-text conversion. Truth and lie statements were extracted from the answers to relevant, irrelevant, and leading questions and were matched with neurolinguistics techniques. In rule-based matching, the XML-Roberta model was used on synchronized sentences to extract phrases or markers for dishonesty and reality. The linguistic patterns and sentence complexity (POS and types of sentences) were excavated. In light of these facts, the reporters use neurolinguistic techniques to extract valid information from the accused or suspects and witnesses. Furthermore, lie statements frequently comprise compound, complex, and compound-complex sentences while truth statements are habitually simple sentences. Moreover, the Roberta-base model is 75% effective for neurolinguistic deception detection.