Monitoring and Analysis of Microblog Messages through Filtering and Magnification

Microblogs are now important platforms for people to communicate their opinions and emotions with others. However, it is difficult to effectively get messages of interest from microblogs, because of their enormous quantity, dynamics and complexity. In this paper, we present an interactive visual analytics system to analyze the data from microblogs. First, an effective SVM classifier is constructed interactively. The SVM classifier and keyword queries are used to filter messages and monitor the topics of interest. Then, we provide several data analysis and visualization tools including statistics tools, heat map, tag cloud. They can assist analysts to explore and understand the data. Additionally, we developed a new illustration method based on video magnification technique to reveal subtle temporal features in the data. The results of our experiment using data from Twitter show that our system can dynamically classify messages according to the topics of interest and reveal tiny patterns in the data from microblogs.