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Time-series Visualization of Twitter Trends

Twitter provides a function called “trend” that presents popular words and hashtags. Typically, one trend word or hashtag is related to thousands of tweets. It is difficult to understand such thousands of tweets in a short time by using the standard sort methods and the standard display method provided by Twitter. Most of previous studies analyzed and visualized tweets by using text-based clustering methods. However, these methods suffer from the accuracy of clustering results, because a typical tweet has only poor textual information. This paper presents a Twitter trend analysis system that combines retweet clustering and timeseries visualization to allow users to understand flows of topics in a Twitter trend in a short time. This system also provides a list of effective legends and a display of individual tweets with photos in order for users to further understand topics in a trend. To illustrate the effectiveness of this system, this paper presents the results of experiments on the analysis of Twitter trends related to a popular sport event and a popular music program.