Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
On the Bursty Evolution of Blogspace
World Wide Web
BlogScope: a system for online analysis of high volume text streams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient term cloud generation for streaming web content
ICWE'10 Proceedings of the 10th international conference on Web engineering
A spatio-temporal framework for related topic search in micro-blogging
AMT'10 Proceedings of the 6th international conference on Active media technology
Understanding a celebrity with his salient events
AMT'10 Proceedings of the 6th international conference on Active media technology
Detecting and exploiting stability in evolving heterogeneous information spaces
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
ACM Transactions on Management Information Systems (TMIS)
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Over the last few years, blogs (web logs) have gained massive popularity and have become one of the most influential web social media in our times. Every blog post in the Blogosphere has a well defined timestamp, which is not taken into account by search engines. By conducting research regarding this feature of the Blogosphere, we can attempt to discover bursty terms and correlations between them during a time interval. We apply Kleinberg's automaton on extracted titles of blog posts to discover bursty terms, we introduce a novel representation of a term's burstiness evolution called State Series and we employ a Euclidean-based distance metric to discover potential correlations between terms without taking into account their context. We evaluate the results trying to match them with real life events. Finally, we propose some ideas for further evaluation techniques and future research in the field.