Once a disaster occurs, people discuss various topics in social media such as electronic bulletin boards, SNSs and video sharing website, and their decision-making tends to be affected by discussions in social media. Under this circumstances, a mechanism to detect topics in social media has become important. This paper targets the East Japan Great Earthquake, and proposes a method for topic discovering from the emergent time series. In this paper, our proposed method analyzes user comments in video sharing websites, and adopts directed graphs to show topic structures in social media. Then clusters are formed using modularity measure which expresses the quality of division of a network into modules or communities. Topic structures are visualized dynamically, so that we can understand emerging topics easily. An experimental result using actual user comments in the video sharing website is shown as well.
