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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Detecting Buzz from Time-Sequenced Document Streams
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
A Novelty-based Clustering Method for On-line Documents
World Wide Web
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Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th international conference on World wide web
On burstiness-aware search for document sequences
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic detection of trends in time-stamped sequences: an evolutionary approach
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Blog Ranking Based on Bloggers' Knowledge Level for Providing Credible Information
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
T-Scroll: visualizing trends in a time-series of documents for interactive user exploration
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
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In this paper, we discuss a method for early detection of "gradual buzzwords" by analyzing time-series data of blog entries. We observe the process in which certain topics grow to become major buzzwords and determine the key indicators that are necessary for their early detection. From the analysis results based on 81,922,977 blog entries from 3,776,154 blog websites posted in the past two years, we find that as topics grow to become major buzzwords, the percentages of blog entries from the blogger communities closely related to the target buzzword decrease gradually, and the percentages of blog entries from the weakly related blogger communities increase gradually. We then describe a method for early detection of these buzzwords, which is dependent on identifying the blogger communities which are closely related to these buzzwords. Moreover, we verify the effectiveness of the proposed method through experimentation that compares the rankings of several buzzword candidates with a real-life idol group popularity competition.