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Blogosphere: research issues, tools, and applications

Published:31 May 2008Publication History
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Abstract

Weblogs, or Blogs, have facilitated people to express their thoughts, voice their opinions, and share their experiences and ideas. Individuals experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their niche needs will be met through online interactions. Its open standards and low barrier to publication have transformed information consumers to producers. This has created a plethora of open-source intelligence, or "collective wisdom" that acts as the storehouse of over-whelming amounts of knowledge about the members, their environment and the symbiosis between them. Nonetheless, vast amounts of this knowledge still remain to be discovered and exploited in its suitable way. In this paper, we introduce various state-of-the-art research issues, review some key elements of research such as tools and methodologies in Blogosphere, and present a case study of identifying the influential bloggers in a community to exemplify the integration of some major aspects discussed in this paper. Towards the end, we also compare and contrast the blogosphere and social networks and the research therein.

References

  1. E. Adar, L. Zhang, L. Adamic, and R. Lukose. Implicit structure and the dynamics of blogspace. In Proceedings of the 13th International World Wide Web Conference, 2004.Google ScholarGoogle Scholar
  2. Nitin Agarwal, Magdiel Galan, Huan Liu, and Shankar Subramanya. Clustering blogs with collective wisdom. In Proceedings of the International Conference on Web Engineering, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Nitin Agarwal, Huan Liu, John J. Salerno, and Philip S. Yu. Searching for Familiar Strangers on Blogosphere: Problems and Challenges. In NSF Symposium on Next-Generation Data Mining and Cyber-enabled Discovery and Innovation (NGDM), 2007.Google ScholarGoogle Scholar
  4. Nitin Agarwal, Huan Liu, Lei Tang, and Philip S. Yu. Identifying the influential bloggers. In Proccedings of the First ACM International Conference on Web Search and Data Mining (Video available at: http://videolectures.net/wsdm08 agarwal iib/), 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Attardi and M. Simi. Blog mining through opinionated words. In Proceedings of the fifteenth Text REtrieval Conference (TREC), 2006.Google ScholarGoogle Scholar
  6. A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286(509), 1999.Google ScholarGoogle Scholar
  7. A. Blanchard. Blogs as virtual communities: Identifying a sense of community in the julie/julia project. Into the Blogosphere: Rhetoric, Community and Culture.http://blog.lib.umn.edu/blogosphere, 2004.Google ScholarGoogle Scholar
  8. A. Blanchard and M. Markus. The experienced sense of a virtual community: Characteristics and processes. The DATA BASE for Advances in Information Systems, 35(1), 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. Blum, T. H. C. Mugizi, and M. R. Rwebangira. A random-surfer web-graph model. In Third Workshop on Analytic Algorithmics and Combinatorics (ANALCO06), 2006.Google ScholarGoogle ScholarCross RefCross Ref
  10. Sergey Brin and Lawrence Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1-7):107--117, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Christopher H. Brooks and Nancy Montanez. Improved annotation of the blogosphere via autotagging and hierarchical clustering. In WWW '06: Proceedings of the 15th international conference on World Wide Web, pages 625--632, New York, NY, USA, 2006. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Alvin Chin and Mark Chignell. A social hypertext model for finding community in blogs. In HYPERTEXT'06: Proceedings of the seventeenth conference on Hypertext and hypermedia, pages 11--22, New York, NY, USA, 2006. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Thayne Coffman and Sherry Marcus. Dynamic classification of groups through social network analysis and hmms. In Proceedings of IEEE Aerospace Conference, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  14. Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. Indexing by latent semantic analysis. Journal of the American Society for information science, 1990.Google ScholarGoogle Scholar
  15. Daniel Drezner and Henry Farrell. The power and politics of blogs. In American Political Science Association Annual Conference, 2004.Google ScholarGoogle Scholar
  16. L. Efimova and S. Hendrick. In search for a virtual settlement: An exploration of weblog community boundaries, 2005.Google ScholarGoogle Scholar
  17. T. Elkin. Just an online minute.. online forecast. http://publications.mediapost.com/index.cfm?fuseaction=Articles.showArticle art aid=29803.Google ScholarGoogle Scholar
  18. Thomas L. Friedman. The World Is Flat: A Brief History of the Twenty-First Century. Farrar, Straus and Giroux, 2005.Google ScholarGoogle Scholar
  19. Michael Gamon, Anthony Aue, Simon Corston-Oliver, and Eric Ringger. Pulse: Mining Customer Opinions from Free Text. In Proceedings of the 6th International Symposium on Intelligent Data Analysis, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kathy E. Gill. How can we measure the influence of the blogosphere? In Proceedings of the WWW'04: work-shop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2004.Google ScholarGoogle Scholar
  21. Dan Gillmor. We the Media: Grassroots Journalism by the People, for the People. O'Reilly, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Jennifer Golbeck and James Hendler. Inferring binary trust relationships in web-based social networks. ACM Trans. Inter. Tech., 6(4):497--529, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Jacob Goldenberg, Barak Libai, and Eitan Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12:211--223, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  24. D. Gruhl, David Liben-Nowell, R. Guha, and A. Tomkins. Information diffusion through blogspace. SIGKDD Exploration Newsletter, 6(2):43--52, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. R. Guha, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins. Propagation of trust and distrust. In WWW '04: Proceedings of the 13th international conference on World Wide Web, pages 403--412, New York, NY, USA, 2004. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Z. Gyongyi, P. Berkhin, Hector Garcia-Molina, and J. Pedersen. Link spam detection based on mass estimation. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Z. Gyongyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB), 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Akshay Java, Pranam Kolari, Tim Finin, and Tim Oates. Modeling the spread of influence on the blogosphere. In Proceedings of the 15th International World Wide Web Conference, 2006.Google ScholarGoogle Scholar
  29. Anubhav Kale, Amit Karandikar, Pranam Kolari, Akshay Java, Tim Finin, and Anupam Joshi. Modeling trust and influence in the blogosphere using link polarity. In International Conference on Weblogs and Social Media, 2007.Google ScholarGoogle Scholar
  30. Ed Keller and Jon Berry. One American in ten tells the other nine how to vote, where to eat and, what to buy. They are The Influentials. The Free Press, 2003.Google ScholarGoogle Scholar
  31. David Kempe, Jon Kleinberg, and Eva Tardos. Maximizing the spread of influence through a social network. In Proceedings of the KDD, pages 137--146, New York, NY, USA, 2003. ACM Press. Google ScholarGoogle Scholar
  32. J. Kleinberg. Authoritative sources in a hyperlinked environment. In 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. P. Kolari, T. Finin, and A. Joshi. SVMs for the blogosphere: Blog identification and splog detection. In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, 2006.Google ScholarGoogle Scholar
  34. P. Kolari, A. Java, T. Finin, T. Oates, and A. Joshi. Detecting spam blogs: A machine learning approach. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Apostolos Kritikopoulos, Martha Sideri, and Iraklis Varlamis. Blogrank: ranking weblogs based on connectivity and similarity features. In AAA-IDEA '06: Proceedings of the 2nd international workshop on Advanced architectures and algorithms for internet delivery and applications, page 8, New York, NY, USA, 2006. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for emerging cyber communities. In The 8th International World Wide Web Conference, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Ravi Kumar, Jasmine Novak, Prabhakar Raghavan, and Andrew Tomkins. On the Bursty Evolution of Blogspace. In Proceedings of the 12th international conference on World Wide Web, pages 568--576, New York, NY, USA, 2003. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs. In SIAM International Conference on Data Mining, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  39. Beibei Li, Shuting Xu, and Jun Zhang. Enhancing clustering blog documents by utilizing author/reader comments. In ACM-SE 45: Proceedings of the 45th annual southeast regional conference, pages 94--99, New York, NY, USA, 2007. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Yu-Ru Lin, Hari Sundaram, Yun Chi, Junichi Tatemura, and Belle L. Tseng. Splog detection using self-similarity analysis on blog temporal dynamics. In Proceedings of the 3rd international workshop on Adversarial information retrieval on the web (AIRWeb), pages 1--8, New York, NY, USA, 2007. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly. Detecting spam web pages through content analysis. In Proceedings of the 15th international conference on World Wide Web (WWW), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.Google ScholarGoogle Scholar
  44. David M. Pennock, Gary W. Flake, Steve Lawrence, Eric J. Glover, and C. Lee Giles. Winners don't take all: Characterizing the competition for links on the web. Proceedings of the National Academy of Sciences, 99(8):5207-5211, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  45. Josep M. Pujol, Ramon Sangesa, and Jordi Delgado. Extracting reputation in multi agent systems by means of social network topology. In Proceedings of the first international joint conference on Autonomous agents and multiagent systems (AAMAS), pages 467--474, New York, NY, USA, 2002. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Matthew Richardson and Pedro Domingos. Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge Discovery and Data mining, pages 61--70, New York, NY, USA, 2002. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Jordi Sabater and Carles Sierra. Reputation and social network analysis in multi-agent systems. In AAMAS '02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems (AAMAS), pages 475--482, New York, NY, USA, 2002. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Loren Terveen and David W. McDonald. Social matching: A framework and research agenda. ACM Trans. Comput.-Hum. Interact., 12(3):401--434, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world networks. Nature, 393(6684):440442, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  50. Bin Yu and Munindar P. Singh. Detecting deception in reputation management. In Proceedings of the second international joint conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 73--80, New York, NY, USA, 2003. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library

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              cover image ACM SIGKDD Explorations Newsletter
              ACM SIGKDD Explorations Newsletter  Volume 10, Issue 1
              June 2008
              50 pages
              ISSN:1931-0145
              EISSN:1931-0153
              DOI:10.1145/1412734
              Issue’s Table of Contents

              Copyright © 2008 Authors

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 31 May 2008

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