Can behavioral features reveal lying in an online personality questionnaire? The impact of mouse dynamics and speech

People who deceive in personality assessment questionnaires can resort to lying in pursuit of socially harmful goals. Efforts to validate the veracity of answers is a complex challenge. Traditional social desirability scales have been scrutinized over discrepancies in the phenomena they capture, while machine learning solutions have been predominantly restricted to artificial laboratory environments. Seeking dependable solutions applicable in realistic online personality questionnaires, we study the effects of real environments and devices on prediction models that utilize current techniques based on the user’s mouse dynamics. Additionally, to improve real-world applicability, we designed an enrichment of the questionnaire where participants verbally elaborate their answers. We conducted an in-the-wild between-subject experiment with 64 participants, obtaining a dataset of 3840 questionnaire item answers, classified as either deceptive or honest based on explicit feedback. Speech-based features were then extracted to evaluate their predictive capability in machine learning and their statistical significance. Results show that in realistic conditions for an online questionnaire, the accuracy of lying prediction from mouse data can decline compared to controlled laboratory settings, and time-based features can lose significance. We identify speech-based features that may mitigate this deficiency. • Realistic conditions impair mouse-based lie detection in personality questionnaire. • Analysis of verbal answer elaborations yields significant indicators of faking good. • Faking good increases articulation rate, decreases lexical diversity. • More nouns and adverbs, more varied verb forms and tenses are found in lies.