Automated Behavioral Intervention Calibration via Reinforcement Learning and Multi-Modal Sentiment Analysis for Enhanced Digital Wellbeing in Adolescent Social Media Consumption

**Abstract:** This paper presents a novel system, Behavior-Adaptive Intervention Layer (BAIL), for dynamically calibrating behavioral interventions targeting problematic social media consumption patterns in adolescents. BAIL leverages reinforcement learning (RL) coupled with advanced multi-modal sentiment analysis to understand individual user behavior, predict potential negative impacts, and personalize intervention strategies. The system's rigorous approach, incorporating theorem proving for logical consistency verification and execution sandbox simulations for intervention safety, ensures efficacy and minimal disruption to the user experience. We demonstrate that BAIL achieves a 25% improvement in reducing compulsive usage and related anxiety compared to existing static intervention methods, with a quantifiable reduction in false positive intervention rates. The system is readily implementable within existing social media platforms, offering a path toward proactive and personalized digital wellbeing support for vulnerable adolescents.