A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Mining geographic knowledge using location aware topic model
Proceedings of the 4th ACM workshop on Geographical information retrieval
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
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There has been an increasing interest in analyzing social network services data. However, detecting social topics in the era of information explosion requires state-of-the-art analytics techniques. The geographic clustering analysis based on social topics across provinces, i.e., states, has rarely been studied. Using the Twitter data collected in the United States (US), we detected the social hot topic by using the ratio of word frequency. Also, we found geographic communities by correlating the time series for a set of topic words across US states. The result of the geographic clustering was visualized using the Google Fusion Table. In conclusion, the ratio of word frequency properly detects social topics or breaking news while suppressing daily tweeted small talks or emotional words such as lol, like, and love. We have also demonstrated that a clustering algorithm based on a social topic can be useful in classifying social communities.