Visual Analytics of User Influence and Location-Based Social Networks

Abstract

Social media have evolved as an important source of information and situational awareness in crisis and emergency management. As the number of messages generated and diffused through social networks in time of crisis increase exponentially, locating reliable and critical information in a timely manner is crucial, especially for decision makers. In such scenarios, identifying influential users in social networks, detecting anomalous information diffusion patterns, and locating corresponding geographical coordinates are often instrumental in providing important information and helping analysts make decisions in a timely manner. We describe a visual analytics framework focusing on identifying influential users and anomalous information diffusion based on dynamic social networks using Twitter data. We also demonstrate a visual analytics approach that allows users to analyze a large volume of social media data to detect and examine abnormal events within Location-Based Social Network (LBSN). Our statistical models to extract user topics and evaluate their anomaly scores are applied to facilitate exploration and perception of Twitter semantics. The framework provides highly interactive filtering and geo-location mapping to help categorize different topics, detect influential users and anomalous information in specific events, and investigate the underlying spatiotemporal patterns.