Abstract: Abundant information can be gained from massive microblog data. Microblogs record the whole process of hot events and people’s reactions. It is increasingly important to obtain meaningful and useful information from microblogs, shape a clear picture of the evolution process of hot event and discover some turning points in the hot event. Existing solutions are mainly based on word frequency, which lacks abstract description to sub-topics. This paper proposes a new interactive visualization method that combines the techniques of topic extraction and word frequency statistics, to visualize the evolution process of sub-topics in different granularities. By observing the variation of word distributions in sub-topics for adjacent time intervals, turning-point events related to some sub-topics can be discovered, and then corresponding contents in the microblog can be tracked with the aid of word co-occurrence graphs. During the interactive process, the parameters in the method can be adjusted by users and optimal values can be eventually determined for a better understanding of turning-point events as well as the evolution process of the hot event. Experiments are conducted on real Sina Weibo datasets, and the results demonstrate that this method is more effective than existing ones based on word frequency and topic trends separately.

Key words: Sub-topic detection , microblog , topic model, visualization