Vol 3 Issue 3 July - Sept 2025

Author Name: Grace Celestina K Date: 27-03-2026 Sentiment analysis has emerged as one of the most widely deployed computational tools for inferring psychological states from digital media behavior, enabling real-time measurement of audience emotional responses at scales impossible through traditional self-report methods. This paper provides a comprehensive evaluation of sentiment analysis as a psychological assessment […]

Author Name:  Ashish K Date: 27-03-2026 The migration of psychological assessment from laboratory to digital and online environments has fundamentally transformed the methodological landscape of media psychology research. This paper provides a comprehensive evaluation of digital psychometric methods used in online media research contexts, examining validity, reliability, and methodological standards for web-based psychological assessment of

Author Name: Aryan Manna K Date: 27-03-2026 The spread of content through digital media networks is governed not only by its informational quality but by its psychological properties, specifically its capacity to activate emotional responses that motivate sharing, amplify attention, and generate social signaling behaviors. This paper examines the psychological dimensions of content virality in digital

Author Name: Niharika Kapoor Date: 27-03-2026 Behavioral prediction systems embedded in digital news platforms increasingly use machine learning to anticipate, segment, and target audiences based on inferred psychological attributes derived from interaction histories, content preferences, and passive engagement signals. This paper provides a comprehensive review of machine learning approaches to behavioral prediction in news contexts,

Author Name: Akhil Date: 27-03-2026 The deployment of artificial intelligence in media recommendation systems has enabled the construction of granular psychological profiles of users based on behavioral trace data, content interaction patterns, and passive digital signals. This paper provides a comprehensive theoretical and empirical analysis of how AI-driven recommendation systems infer, represent, and operationalize psychological constructs