Organization of social network messages to improve understanding of an evolving crisis

In this work, we present a system that is implemented on top of a stream of social network messages to facilitate learning about an emerging crisis. This method automatically detects sub-topics of the topic (crisis), and populates the sub-topics with the relevant retrieved messages. In future work, we intend to reduce the repetitiveness of the generated sub-topics while maintaining high precision in our classification, and implementing other unsupervised learning versions of our method and comparing them to the KNN version.