Abstract: Social networks, such as Twitter and Sina weibo, have become popular platforms to report the public event. They provide valuable data for us to monitor events and their evolution. However, informal words and fragmented texts make it challenging to extract descriptive information. Monitoring the event progression from fast accumulation of microblogs is also difficult. To this end, we monitor the event progression with a common topic from the social network. This can help us to gain an overview and a detailed documentation of the events. In this paper, we use three consecutive components to meet this end. First, we use a structure based approach to detect events from the microblog dataset. Second, we cluster the events by their topics based on their latent semantic information, and define each cluster as a story. Third, we use a graph based approach to generate a storyline for each story. The storyline is denoted by a directed acyclic graph (DAG) with a summary to express the progression of events in the story. The user experience evaluation indicates that this method can help us to monitor events and their progression by achieving improved accuracy and comprehension compared with the state of art methods.

Key words: social network, event progression, storyline, cluster, topic model