PREDICTING FAKE NEWS SUSCEPTIBILITY AMONG TECHNICALLY SKILLED EMPLOYEES USING PSYCHOLOGICAL TRAITS

Abstract

In a time characterized by the quick development of digital information, technically skilled workerswho are trained to think in logical fashionsare still vulnerable to misinformation. Fake news which attends to cybersecurity, engineering trends, or technologies used in the workplace can negatively affect a professional decision, team engagement, or trust in a colleague or organization. This paper will present how personal psychological characteristics like need for cognition, openness to experience, cognitive reflection and confirmation bias predict vulnerability to fake news by technically skilled workers. In this paper, the detailed findings of a varied psychometric and machine-learning classifier analysis of 310 employees in the IT, engineering, and data science fields is discussed. Participants were asked to fill out validated inventories measuring psychological traits and answer a selection of reality and fake news headlines which were grounded in technical fields. Lower cognitive reflection scores, higher confirmation bias scores predict vulnerability to fake information. Need for cognition only acted as a protective factor, when moderated by lower impulsivity.This paper presents a predictive model, using logistic regression and random forest classifiers, that achieved an over 82% accuracy to predict an individual's tendency to believe or share fake news. The model emphasizes the mediating influence of psychological patterns vignette the mere possession of domain knowledge. The findings of this paper demonstrate the necessity for tailored interventions to implement metacognitive training and internal fact-checking protocols within organizations to better reduce the dissemination of misinformation. This research is positioned as a contribution to the organizational psychology and digital trust scholarship, and offers possible strategies to protect technically skilled workforces from cognitive manipulation in environments associated with high-stakes information.