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Algorithmic Bias in News Recommendation: Psychological Consequences for Marginalized Audiences

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 […]

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Filter Bubbles and Epistemic Isolation: Psychological Mechanisms and Measurement in Algorithmically Curated News

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

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Echo Chambers and Psychological Entrenchment: How Recommendation Algorithms Reinforce Existing Beliefs

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

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Data-Driven Audience Segmentation and Psychological Profiling: Methodological Advances and Ethical Constraints

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

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Predictive Modeling of News Engagement: Machine Learning Approaches to Audience Psychology

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

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Psychometric Properties of Big Data-Derived Audience Measures: Validity, Reliability, and Construct Equivalence

Author Name: Kanwar AdhiRaj Singh Jodha Date: 27-03-2026 Big data methodologies have transformed audience measurement in journalism, replacing sample-based surveys with census-level behavioral observation of digital news consumption. Yet the psychometric properties of big data-derived audience measures — their validity, reliability, and construct equivalence across demographic groups and platforms — remain largely unexamined, creating an

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Doomscrolling, Compulsive News Monitoring, and Psychological Distress: Toward a Validated Assessment Framework

Author Name: Abhishikth Gajulavarti Date: 27-03-2026 Doomscrolling — the compulsive consumption of negative news content despite awareness of resultant distress — emerged as a widely recognized behavioral phenomenon during the COVID-19 pandemic and has persisted as a clinically significant pattern across subsequent crises. Despite its prevalence and popular attention, doomscrolling lacks a consensus definition, a

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Depression Screening in Social Media Contexts: Passive Sensing, Natural Language Processing, and Ethical Boundaries

Author Name: Prakhar Shankar Date: 27-03-2026 Social media platforms have become repositories of psychologically rich behavioral data whose analysis offers unprecedented opportunities for population-level depression screening and individual-level clinical assessment. This paper provides a comprehensive review of passive sensing and natural language processing (NLP) approaches to depression detection from social media data, evaluating their validity, clinical

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Social Media Exposure and Anxiety: Measuring the Dose-Response Relationship Through Digital Biomarkers

Author Name: Madhav Menon Date: 27-03-2026 The relationship between social media exposure and anxiety has become one of the most contested empirical questions in contemporary psychological science, with studies producing contradictory findings that reflect methodological heterogeneity rather than genuine theoretical inconsistency. This paper provides a comprehensive theoretical and empirical review of the social media-anxiety association, with

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Mobile Ecological Momentary Assessment in Media Psychology: Capturing Real-Time Psychological Responses to Digital News Exposure

Author Name: Niharika Kapoor Date: 27-03-2026 Ecological Momentary Assessment (EMA) delivered through mobile smartphones offers media psychology research a methodological breakthrough in capturing psychological responses to digital media exposure in real time, in natural contexts, and at ecological validity levels impossible in traditional laboratory or survey designs. This paper provides a comprehensive review and methodological framework

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