EQSA: Earthquake Situational Analytics from Social Media This paper introduces EQSA, an interactive exploratory tool for earthquake situational analytics using social media. EQSA is designed to support users to characterize the condition across the area around the earthquake zone, regarding related events, resources to be allocated, and responses from the community. On the general level, changes in the volume of messages from chosen categories are presented, assisting users in conveying a general idea of the condition. More indepth analysis is provided with topic evolution, community visualization, and location representation. EQSA is developed with intuitive, interactive features and multiple linked views, visualizing social media data, and supporting users to gain a comprehensive insight into the situation. In this paper, we present the application of EQSA with the VAST Challenge 2019: MiniChallenge 3 (MC3) dataset.

Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of realtime, live data about true physical events from around the world. However, while such human sensor data have been exploited for retrospective largescale event detection such as hurricanes or earthquakes, there has been limited to no success in exploiting this rich resource for general physical event detection. Prior implementation approaches have suffered from the concept drift phenomenon, where realworld data exhibits continuous, unknown, and unbounded changes in its data distribution, making static machine learning models ineffective in the long term. We propose and implement an endtoend collaborative drift adaptive system that integrates corroborative and probabilistic sources to deliver realtime predictions. Furthermore, our system is adaptive to concept drift and performs automated continuous learning to maintain high performance. We demonstrate our approach in a realtime demo available online for landslide disaster detection, with extensibility to other realworld physical events such as flooding, wildfires, hurricanes, and earthquakes.