Social media sensing has emerged as a new disaster response application paradigm to collect real-time observations from online social media users about the disaster status. Due to the noisy nature of social media data, the task of identifying trustworthy information (referred to as “truth discovery”) has been a crucial task in social media sensing. In this project, we develop SocialDrone, a novel closed-loop social-physical active sensing framework that integrates social media and unmanned aerial vehicles (UAVs) for reliable disaster response applications. In SocialDrone, signals emitted from the social media are distilled to drive the drones to target areas to verify the emergency events. The verification results are then taken back to improve the sensing and distillation process on social media. The SocialDrone framework introduces several unique challenges: i) how to drive the drones using the unreliable social media signals? ii) How to ensure the system is adaptive to the high dynamics from both the physical world and social media? iii) How to incorporate real-world constraints (e.g., the deadlines of events, limited number of drones) into the framework? iv) How to optimize the drone deployment by exploring the highly dynamic and latent correlations among event locations? v) How to satisfy the conflicting objectives of event coverage of the application and energy conservation of drones? The SocialDrone project addresses these challenges by building a novel integrated social-physical sensing system that leverages techniques from game theory, constrained optimization, spatiotemporal correlation inference model and reinforcement learning. The results have been published in IEEE INFOCOM 2020, ICCCN 2019, and ICPADS 2019.