This study conceptualizes digital platforms as operating within an “economics of attention,” where user engagement serves as a primary measure of value. Within this system, algorithms function as “emotional algorithms,” interpreting signals such as likes, shares, and clicks as indicators of relevance and prioritizing content that elicits strong emotional responses. As emotionally resonant content is amplified, it shapes patterns of attention, reinforces identity-based alignment, and contributes to affective polarization over time. Using a qualitative comparative analysis of industry trade publications and scholarly sources, this study examines how algorithmic and AI-driven communication systems are framed across public relations, advertising, and marketing. The findings reveal a divergence in discourse: industry sources tend to frame these systems as tools for efficiency and performance, while academic perspectives emphasize their emotional, ethical, and social consequences. To bridge this gap, the study proposes a framework for practitioners that prioritizes intentionality, human judgment, and ethical awareness in AI-driven communication.
Avery Barber (Fri,) studied this question.