CONCEPT
Engagement Optimization
The algorithmic practice of selecting content to maximize time-on-platform — the operational mechanism through which the attention economy degrades democratic deliberation.
Engagement optimization is the specific algorithmic practice that converts the attention economy's business model into observable content curation decisions. Platforms measure user engagement — time-on-platform, clicks, shares, comments, emotional reactions — and train recommendation systems to maximize these metrics. The optimization is value-neutral in its mathematical formulation but catastrophically non-neutral in its effects. Content producing strong emotional reactions — outrage, fear, tribal solidarity, moral indignation — reliably outperforms content informing rational deliberation. The algorithms therefore amplify the former and suppress the latter, not through explicit design but as the predictable output of the optimization target.
In The You On AI Field Guide
The mechanism operates at multiple scales simultaneously. Individual-level personalization identifies each user's specific cognitive vulnerabilities and serves content that exploits them — conspiratorial thinking gets more conspiracy, susceptibility to outrage gets more outrage, vulnerability to social comparison gets more comparison triggers. Population-level dynamics amplify the most engaging content across user bases, producing the viral dynamics that have characterized the social media era. Cross-platform dynamics create competitive pressure that prevents unilateral reform — a platform
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