Searching for events in the blogosphere

ABSTRACT

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.

REFERENCES

• 1: Nilesh Bansal , Nick Koudas, BlogScope: a system for online analysis of high volume text streams, Proceedings of the 33rd international conference on Very large data bases, September 23-27, 2007, Vienna, Austria
• 2: Jon Kleinberg, Bursty and Hierarchical Structure in Streams, Data Mining and Knowledge Discovery, v.7 n.4, p.373-397, October 2003 [doi>10.1023/A:1024940629314]
• 3: Ravi Kumar , Jasmine Novak , Prabhakar Raghavan , Andrew Tomkins, On the Bursty Evolution of Blogspace, World Wide Web, v.8 n.2, p.159-178, June 2005 [doi>10.1007/s11280-004-4872-4]
• 4: Platakis, M., Kotsakos, D., Gunopulos, D. Discovering Hot Topics in the Blogosphere. In Proc. of the 2nd Panhellenic Scientific Student Conference on Informatics, Related Technologies and Applications EUREKA 2008, pp. 122--132.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
blogs, burst analysis, hot topics, information retrieval, keyword correlation, social media, text mining