Vol 4 Issue 1 Jan - Mar 2026

Author Name: Ashish K Date: 28-03-2026 Algorithmic bias in news recommendation systems — systematic patterns of error or distortion in how recommendation algorithms treat different user groups, content types, and journalistic topics — produces psychological consequences that fall disproportionately on already marginalized audiences. This paper provides a comprehensive analysis of the sources, manifestations, and psychological […]

Author Name: Mrs. Sonal Agarwal Date: 28-03-2026 Filter bubbles personalized information environments in which algorithmic curation systematically narrows the diversity of perspectives, sources, and topics to which users are exposed represent a theoretically distinctive concept from echo chambers, yet the two are routinely conflated in both academic literature and public discourse. This paper provides the

Author Name: Niharika Kapoor Date: 28-03-2026 Echo chambers-information environments in which individuals are primarily exposed to viewpoints consonant with their own beliefs-have become a central concern in democratic theory and media psychology, yet the empirical evidence for algorithmic echo chambers is more contested than popular discourse acknowledges. This paper provides a comprehensive, evidence-based review of the

Author Name: Mr. Tarun Panda Date: 28-03-2026 Data-driven audience segmentation has evolved from demographic categorization through behavioral clustering to psychographic profiling that infers psychological characteristics — personality traits, values, motivational orientations, political ideologies — from digital behavioral traces. This evolution has profound implications for both the scientific study of audiences and the ethical governance of media

Author Name: Kanwar AdhiRaj Singh Jodha Date: 28-03-2026 Machine learning methods have enabled the construction of news engagement prediction models of unprecedented predictive power, yet the psychological interpretability of these models-what they reveal about the psychological processes driving engagement-remains limited by a fundamental tension between predictive performance and explanatory transparency. This paper reviews the state