`LinkedIn, LinkedIn on the screen, who is the greatest and smartest ever seen?': A machine learning approach using valid LinkedIn cues to predict narcissism and intelligence - Tobias M. Härtel; Benedikt A. Schuler; Mitja D. Back (Journal of Occupational and Organizational Psychology, 2024)
Abstract Recruiters routinely use LinkedIn profiles to infer applicants' individual traits like narcissism and intelligence, two key traits in online network and organizational contexts. However, little is known about LinkedIn profiles' predictive potential to accurately infer individual traits. According to Brunswik's lens model, accurate trait inferences depend on (a) the presence of valid cues in LinkedIn profiles containing information about users' individual traits and (b) the sensitive and consistent utilization of valid cues. We assessed narcissism (self-report) and intelligence (aptitude tests) in a sample of 406 LinkedIn users along with 64 LinkedIn cues (coded by three trained coders) that we derived from trait theory and previous empirical findings. We used a transparent, easy-to-interpret machine learning algorithm leveraging practical application potentials (elastic net) and applied state-of-the-art resampling techniques (nested cross-validation) to ensure robust results. Thereby, we uncover LinkedIn profiles' predictive potential: (a) LinkedIn profiles contain valid information about narcissism (e.g. uploading a background picture) and intelligence (e.g. listing many accomplishments), and (b) the elastic nets sensitively and consistently using these valid cues attain prediction accuracy ( r 0.167em=0.167em.35/.41 for narcissism/intelligence). The results have practical implications for improving recruiters' accuracy and foreshadow potentials and limitations of automated LinkedIn-based assessments for selection purposes.
Keywords: 330; Brunswikian lens model; cybervetting; LinkedIn; machine learning; trait assessment