Analyzing and Tracking Weblog Communities Using Discriminative Collection Representatives

  • Guozhu Dong
  • Ting Sa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6007)


Analyzing/tracking weblogs by given communities (ATWC) is increasingly important for sociologists and government agencies, etc. This paper introduces an approach to address the needs of ATWC by using concise discriminative weblog collection representatives (DCRs). DCRs are aimed at helping users to quickly identify the major themes/trends in such collections, and to quickly identify important shifts/differences in major themes and trends of blogs by given communities over time and space. We propose to use the quality of DCR-based classifiers to measure DCRs’ quality. We present algorithms for constructing DCRs, report experimental results to evaluate the efficiency of the algorithms and the quality of the DCRs they construct, and provide real-data examples to demonstrate the usefulness of DCRs for ATWC.


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  1. 1.
    Agarwal, N., Kumar, S., Liu, H., Woodward, M.: BlogTrackers: A Tool for Sociologists to Track and Analyze Blogosphere. In: AAAI Conf. on Weblogs and Social Media (2009)Google Scholar
  2. 2.
    Agarwal, N., Liu, H., Tang, L., Yu, P.: Identifying Influential Bloggers in a Community. In: Intl. Conf. on Web Search and Data Mining (2008)Google Scholar
  3. 3.
    Carbonell, J., Goldstein, J.: The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR, 335–336 (1998)Google Scholar
  4. 4.
    Chen, L., Dong, G.: Succint and informative cluster descriptions for document repositories. In: Int’l. Conf. on Web-Age Information Management (2006)Google Scholar
  5. 5.
    Fisher, S., Roark, B.: OGI/OHSU Basline Multilingual Multi-document Summarization System. Mutli-lingual Summarization Evaluation (2005)Google Scholar
  6. 6.
    Lin, C.Y., Hovy, E.: Automated multi-document summarization in neats. In: Proceedings of the Human Language Technology Conference (2002)Google Scholar
  7. 7.
    Metzler, D., Kanungo, T.: Machine learned sentence selection strategies for query-biased summarization. In: SIGIR Learning to Rank Workshop (2008)Google Scholar
  8. 8.
    Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Conference on Empirical Methods in Natural Language Processing (2004)Google Scholar
  9. 9.
    Shen, D., Sun, J.T., Li, H., Yang, Q., Chen, Z.: Document summarization using conditional random fields. IJCAI (2007)Google Scholar
  10. 10.
    Zafarani, R., Liu, H.: Social Computing Data Repository at ASU. In: School of Computing, Informatics and Decision Systems Engineering, Arizona State University (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Guozhu Dong
    • 1
  • Ting Sa
    • 1
  1. 1.Department of Computer Science and EngineeringWright State UniversityDaytonUSA

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