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 news environments, analyzing how AI systems model emotional contagion mechanisms and deploy them for engagement optimization. Drawing on emotional contagion theory (Hatfield et al., 1993), the dual-process model of news sharing (Berger, 2011), and computational affective analysis research, the paper proposes an integrated Affective Virality Architecture that maps psychological mechanisms of digital content spread to the algorithmic systems that detect, amplify, and exploit them. The paper reviews the Massive-Scale Emotional Contagion study (Kramer et al., 2014; N = 689,003), moral outrage amplification findings from Twitter engagement research, VADER and transformer-based sentiment analysis validation studies, and the emerging neuroscience of viral emotion. Computational models for predicting content virality are evaluated for their psychological validity and ethical implications. The paper demonstrates that AI virality models have converged on high-arousal negative emotions as the most reliable behavioral amplifiers, creating systematic incentives for negative content production. An alternative affective optimization framework prioritizing emotional diversity, epistemic curiosity, and prosocial emotion induction is proposed.
Keywords: emotional contagion; content virality; affective computing; moral outrage; sentiment analysis; algorithmic amplification; prosocial emotion; news sharing.
