Contextual audience construction has long relied on rule-based keyword and taxonomy matching, often applied as touch-based frequency rules. As third-party tracking weakens, contextual audience construction gains prominence, while embedding-based semantic cohorting emerges as a plausible alternative to represent intent under privacy constraints. In most workflows, advertisers provide targeting terms, and platforms determine how inputs are translated into deliverable cohorts. This motivates a practical product question: do keyword/touch-based cohorts reflect stated intent, and can embedding-based cohorting improve suitability without unacceptable scale loss? Using the Microsoft News Dataset (MIND), we compare classical touch-based cohorting versus semantic cohorting through controlled offline experiments. Under strict leakage prevention and chronological holdout, we audit resulting audiences using structural diagnostics (constraint violation rate, topic drift, coverage, concentration) and a blind held-out click proxy. Touch-based cohorts maximize reach, but exhibit a higher negative-topic association. Centroid-based semantic cohorting reduces negative-topic association by 27 K=10,000 while reducing inventory coverage. Brief-based intent embeddings can increase topical drift, whereas centroid cohorts improve alignment at larger audience sizes. Human-authored briefs under-perform empirical seed-based definitions. The contribution is primarily conceptual and evaluative: it frames contextual targeting as an audience-construction problem and introduces a structural audit framework for comparing the cohorts produced by different intent-encoding choices.
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