Behavioral signature of the Algorithmic Self in AI-filtered digital environments

Aim: To examine the behavioral signature of the “Algorithmic Self,” characterizing how users adapt their identity and behaviors in response to algorithmic reinforcement among active digital media users in Pakistan. Methods: A cross-sectional quantitative design was employed with 422 adults aged 18–45 years across five major cities. Participants completed a structured online questionnaire capturing demographic data, digital usage patterns, the Algorithmic Exposure Score (AES), and Algorithmic Self Behavioral Signature Scale (ASBSS). Validated instruments assessed social comparison, Fear of Missing Out (FoMO), self-esteem, and digital stress. Data were analyzed using descriptive statistics, Pearson correlations, and multiple linear regression in SPSS version 26, with significance set at p < 0.05. Results: Participants demonstrated moderate-to-high levels of Algorithmic Self formation, with 39.8% classified in the high category. Higher daily screen time, greater platform diversity, stronger algorithmic trust, and elevated social comparison were associated with higher Algorithmic Self Scores. In multiple linear regression analysis, daily screen time (β = 0.34), social comparison (β = 0.31), algorithmic trust (β = 0.29), and algorithmic exposure (β = 0.28) emerged as significant predictors of Algorithmic Self formation, while FoMO was not a significant predictor (β = 0.11, p = 0.09). The final model explained 56% of the variance in Algorithmic Self formation (R2 = 0.56, adjusted R2 = 0.54, p < 0.001). Conclusions: AI-driven digital environments are associated with self-presentation, identity adaptation, and behavioral regulation among Pakistani users. These findings highlight the importance of enhancing digital literacy, improving awareness of algorithmic influence, and further investigating the psychological and societal implications of Algorithmic Self formation in digitally mediated environments.