Distinctive features of nonverbal behavior and mimicry in application interviews through data analysis and machine learning

• Main Authors: ROGIERS, Sanne, CORNEILLIE, Elias, LIEVENS, Filip, ANSEEL, Frederik, VEELAERT, Peter, PHILIPS, Wilfried
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Mimicry; Nonverbal behavior; Data analysis; Machine-learning; Classification experiment; Feature selection; Databases and Information Systems; Organizational Behavior and Theory
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7033; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8031/viewcontent/1_s2.0_S2666827022000366_main.pdf
• Institution: Singapore Management University

This paper reveals the characteristics and effects of nonverbal behavior and human mimicry in the context of application interviews. It discloses a novel analyzation method for psychological research by utilizing machine learning. In comparison to traditional manual data analysis, machine learning proves to be able to analyze the data more deeply and to discover connections in the data invisible to the human eye. The paper describes an experiment to measure and analyze the reactions of evaluators to job applicants who adopt specific behaviors: mimicry, suppress, immediacy and natural behavior. First, evaluation of the applicant qualifications by the interviewer reveals how behavioral self-management can improve the interviewer’s opinion of the candidate. Secondly, the underlying mechanics of mimicry behavior are exposed through analysis of seven nonverbal actions. Manual data analysis determines the frequency features of the actions and answers how often the actions are performed and how often they are mimicked during application interviews. Two of the seven actions are here deemed negligible due too low frequency features. Finally, machine learning is employed to analyze the data in great detail and distinguish the four behavior categories from each other. A Random Forest classifier is able to achieve 55.2% accuracy for predicting the behavior condition of the interviews while human observers reach an accuracy of 32.9%. The feature set for the classifier is reduced to 130 features with the most important features relating to the correlations between the leaning forward actions of the interview participants.