Social Effects of ML in TV Marketing Analytics: Targeting, Addiction, and Child Development

Abstract

The integration of machine learning (ML) in television marketing analytics represents a technological evolution with profound societal implications, particularly concerning mobile gaming advertisements targeting children. Television remains a dominant advertising medium, with gaming companies leveraging advanced ML algorithms to optimize ad placement and maximize conversion rates. These algorithms analyze extensive viewing data to identify optimal broadcasting moments, significantly enhancing marketing efficiency while reducing costs. Concurrently, mobile gaming has embraced freemium business models that generate substantial revenues through in-app purchases, primarily from a small segment of high-value users. The sophisticated targeting capabilities enabled by ML raise critical concerns regarding ethical responsibility and developmental impacts on young viewers. Neuroimaging evidence indicates that children exhibit heightened susceptibility to engagement-maximizing design elements in mobile games, with potential consequences for attention, impulse control, and cognitive development. Additionally, increased gaming engagement correlates with reduced participation in essential developmental activities and diminished family communication. Educational disparities in parental media literacy further exacerbate these challenges, creating unequal protective factors across socioeconomic groups. The confluence of algorithmically optimized persuasion and addictive product design presents unique challenges that warrant scholarly attention and policy consideration to safeguard vulnerable populations in an increasingly algorithmic advertising ecosystem.