Mining blog stories using community-based and temporal clustering

ABSTRACT

In recent years, weblogs, or blogs for short, have become an important form of online content. The personal nature of blogs, online interactions between bloggers, and the temporal nature of blog entries, differentiate blogs from other kinds of Web content. Bloggers interact with each other by linking to each other's posts, thus forming online communities. Within these communities, bloggers engage in discussions of certain issues, through entries in their blogs. Since these discussions are often initiated in response to online or offline events, a discussion typically lasts for a limited time duration. We wish to extract such temporal discussions, or stories, occurring within blogger communities, based on some query keywords. We propose a Content-Community-Time model that can leverage the content of entries, their timestamps, and the community structure of the blogs, to automatically discover stories. Doing so also allows us to discover hot stories. We demonstrate the effectiveness of our model through several case studies using real-world data collected from the blogosphere.

REFERENCES

• 1: Lada A. Adamic , Natalie Glance, The political blogosphere and the 2004 U.S. election: divided they blog, Proceedings of the 3rd international workshop on Link discovery, p.36-43, August 21-25, 2005, Chicago, Illinois [doi>10.1145/1134271.1134277]
• 2: Eytan Adar , Lada A. Adamic, Tracking Information Epidemics in Blogspace, Proceedings of the The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), p.207-214, September 19-22, 2005 [doi>10.1109/WI.2005.151]
• 3: David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003
• 4: Blogger. www.blogger.com.
• 5: Blogpulse. www.blogpulse.com.
• 6: Douglass R. Cutting , David R. Karger , Jan O. Pedersen , John W. Tukey, Scatter/Gather: a cluster-based approach to browsing large document collections, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.318-329, June 21-24, 1992, Copenhagen, Denmark [doi>10.1145/133160.133214]
• 7: Natalie Glance , Matthew Hurst , Kamal Nigam , Matthew Siegler , Robert Stockton , Takashi Tomokiyo, Deriving marketing intelligence from online discussion, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081919]
• 8: D. Gruhl , David Liben-Nowell , R. Guha , A. Tomkins, Information diffusion through blogspace, ACM SIGKDD Explorations Newsletter, v.6 n.2, p.43-52, December 2004 [doi>10.1145/1046456.1046462]
• 9: T. Hoffman. Probabalistic latent semantic analysis. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 1999.
• 10: iBoogie. www.iboogie.com.
• 11: K. Ishida. Extracting latent weblog communities: A partitioning algorithm for bipartite graphs. In Proceedings of 2nd Annual Workshop on the Weblogging Ecosystem, 2005.
• 12: X. Jhu, Z. Ghahramani, and J. Lafferty. Time-sensitive dirichlet process mixture models. Technical Report, CMU-CALD-05-104, 2005.
• 13: C. Kemp, T. L. Griffiths, and J. Tenenbaum. Discovering latent classes in relational data. Technical Report, MIT CSAIL, 2004.
• 14: Jon Kleinberg, Bursty and hierarchical structure in streams, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775061]
• 15: Ravi Kumar , Jasmine Novak , Prabhakar Raghavan , Andrew Tomkins, On the bursty evolution of blogspace, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775233]
• 16: Ravi Kumar , Uma Mahadevan , D. Sivakumar, A graph-theoretic approach to extract storylines from search results, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014078]
• 17: S. law, O. Jerzy, and S. Dawid. Lingo: Search results clustering algorithm based on singular value decomposition, 2004.
• 18: LiveJournal. www.livejournal.com.
• 19: Apache Lucene. lucene.apache.org.
• 20: Michal Rosen-Zvi , Thomas Griffiths , Mark Steyvers , Padhraic Smyth, The author-topic model for authors and documents, Proceedings of the 20th conference on Uncertainty in artificial intelligence, p.487-494, July 07-11, 2004, Banff, Canada
• 21: Junshui Ma , Simon Perkins, Online novelty detection on temporal sequences, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956828]
• 22: A. McCallum, A. Corrada-Emmanuel, and X. Wang. The author-recipient-topic model for topic and role discovery in social networks: Experiments with enron and academic email. Technical Report UM-CS-2004-096, 2004.
• 23: Qiaozhu Mei , ChengXiang Zhai, Discovering evolutionary theme patterns from text: an exploration of temporal text mining, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081895]
• 24: K. Nowicki and T. A. Snijders. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 2001.
• 25: Google Blog Search. blogsearch.google.com.
• 26: Xiaodan Song , Ching-Yung Lin , Belle L. Tseng , Ming-Ting Sun, Modeling and predicting personal information dissemination behavior, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081925]
• 27: Technorati. www.technorati.com.
• 28: B. L. Tseng, J. Tatemura, and Y. Wu. Tomographic clustering to visualize blog communities as mountain views. In Proceedings of 2nd Annual Workshop on the Weblogging Ecosystem, 2005.
• 29: Vivisimo. www.vivisimo.com.
• 30: Xuerui Wang , Natasha Mohanty , Andrew McCallum, Group and topic discovery from relations and text, Proceedings of the 3rd international workshop on Link discovery, p.28-35, August 21-25, 2005, Chicago, Illinois [doi>10.1145/1134271.1134276]
• 31: Oren Zamir , Oren Etzioni, Grouper: a dynamic clustering interface to Web search results, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.31 n.11-16, p.1361-1374, May 17, 1999
• 32: Hua-Jun Zeng , Qi-Cai He , Zheng Chen , Wei-Ying Ma , Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009030]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Algorithms, Experimentation

Keywords:
online-communities, time-sensitive clustering, weblogs