Semiannual Publication
Abstract
This study explores psychological and demographic characteristics distinguishing social media influencers from non-influencers and investigates the predictive potential of psychological features for influence. Using a diverse dataset containing age, gender, NEO personality scores, and a revised active/passive engagement scale of 1,214 Iranian participants, we aim to uncover significant feature differences and construct a predictive model for influence classification. Our statistical analyses reveal significant differences between influencers and non-influencers in key variables, including age and active/passive engagement and Neuroticism. However, machine learning models indicate that while distinct psychological characteristics are associated with influence, their predictive power shows promise but may be limited without additional behavioral or content-based metrics. This study contributes to the understanding of psychological factors in social influence and the feasibility of machine learning models for influencer identification.
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