 Social sensing enables all citizens to become part of a large sensor network, but its massive, heterogeneous, noisy, unreliable data collected in continuous streams often lacks geospatial reference information, posing a grand challenge for fully leveraging social sensing for emergency management decision-making under extreme duress. Big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to disaster events in a timely fashion. This special issue captures recent advancements in leveraging social sensing and big data computing for supporting disaster management, specifically analyzing promises and pitfalls of social sensing data for disaster-relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems. This article was authored by Chen Longli, Kunying Huang, and Christopher T. Enrich. We are article.tv, links in the description below.