Dynamic mapping of dense geo-tweets and web pages based on spatio-temporal analysis

Abstract

Twitter evidently stirred a popular trend of personal update sharing. Twitter users can be kept up to date with current information from Twitter; however, users cannot obtain the most recent information, while they browse web pages since these are not updated in real time. Meanwhile, Twitter users are difficult to gain useful information about their current locations since these are often posted on web pages. To solve them, it is important to enrich traditional web pages with real time tweets. Therefore, we developed a novel tweet mapping system to support web and Twitter user communication through both the contents of tweets and web pages based on spatio-temporal analysis. Our system maps geo-tagged tweets to web pages by matching their location names, and categorizes tweets based on category names of floors from web pages according to different time frames. Thus, our system can effectively present the most related tweets and their summary to help users easily gain more detailed current situation in different time periods, and it also can effectively present messages from web users to help Twitter users immediately obtain useful information. In this paper, we discuss our proposed mapping method's effectiveness with our prototype system using dense tweets in urban areas.

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