A framework for intelligence analysis using spatio-temporal storytelling

Arnold Boediardjo, Feng Chen, Chang-Tien Lu, Patrick Butler, Naren Ramakrishnan

Abstract

Social media have ushered in alternative modalities to propagate news and developments rapidly. Just as traditional IR matured to modeling storylines from search results, we are now at a point to study how stories organize and evolve in additional mediums such as Twitter, a new frontier for intelligence analysis. This study takes as input news articles as well as social media feeds and extracts and connects entities into interesting storylines not explicitly stated in the underlying data. First, it proposes a novel method of spatio-temporal analysis on induced concept graphs that models storylines propagating through spatial regions in a time sequence. Second, it describes a method to control search space complexity by providing regions of exploration. And third, it describes ConceptRank as a ranking strategy that differentiates strongly-typed connections from weakly-bound ones. Extensive experiments on the Boston Marathon Bombings of April 15, 2013 as well as socio-political and medical events in Latin America, the Middle East, and the United States demonstrate storytelling’s high application potential, showcasing its use in event summarization and association analysis that identifies events before they hit the newswire.