Managing the privacy of social media posts remains a complex task, especially as audience diversity and content sensitivity grow. We propose a comprehensive privacy management framework that combines post content features with behavioral signals from social interactions to deliver personalized audience recommendations. Leveraging Facebook and Reddit datasets, we implement five core modules: post privacy classification, persona contradiction detection, interaction and privacy alignment scoring, expectation mismatch analysis, and privacy-aware friend grouping. Our post classifier achieves F1-scores of 0.76 (Facebook) and 0.72 (Reddit); contradiction detection yields an F1-score of 0.80 by combining behavioral and BERT-based features. Friend clustering based on interaction and alignment scores results in silhouette scores of 0.65 (Facebook) and 0.60 (Reddit), while expectation mismatch analysis reveals stronger emotional alignment in highly private posts. Overall, our approach enables explainable and behavior-sensitive audience control for social platforms, improving upon prior content- or interaction-only methods.