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Ranking User Influence in Healthcare Social Media

Published:01 September 2012Publication History
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Abstract

Due to the revolutionary development of Web 2.0 technology, individual users have become major contributors of Web content in online social media. In light of the growing activities, how to measure a user’s influence to other users in online social media becomes increasingly important. This research need is urgent especially in the online healthcare community since positive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence within the online healthcare community. We proposed a new approach to incorporate users’ reply relationship, conversation content and response immediacy which capture both explicit and implicit interaction between users to identify influential users of online healthcare community. A weighted social network is developed to represent the influence between users. We tested our proposed techniques thoroughly on two medical support forums. Two algorithms UserRank and Weighted in-degree are benchmarked with PageRank and in-degree. Experiment results demonstrated the validity and effectiveness of our proposed approaches.

References

  1. Anagnostopoulos, A., Kumar, R., and Mahdian, M. 2008. Influence and correlation in social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 7--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ariely, D., Au, W. T., Bender, R. H., Budescu, D. V., Dietz, C. B., Gu, H., Wallsten, T. S., and Zauberman, G. 2000. The effects of averaging subjective probability estimates between and within judges. J. Exp. Psych. 6, 2, 130--147.Google ScholarGoogle Scholar
  3. Bakeman, R. and Gottman, J. 1997. Observing Interaction: An Introduction to Sequential Analysis. Cambridge University Press.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bermingham, A. and Smeaton, A. F. 2009. A study of inter-annotator agreement for opinion retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Bian, J., Liu, Y., Zhou, D., Agichtein, E., and Zha, H. 2009. Learning to recognize reliable users and content in social media with coupled mutual reinforcement. In Proceedings of the 18th International Conference on World Wide Web. 51--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Campbell, C. S., Maglio, P. P., Cozzi, A., and Dom, B. 2003. Expertise identification using email communications. In Proceedings of the 12th International Conference on Information and Knowledge Management. ACM, 528--531. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Chen, W., Wang, Y., and Yang, S. 2009. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 199--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chuang, K. and Yang, C .C. 2010. Social Support in Online Healthcare Social Networking. In Proceedings of the iConference.Google ScholarGoogle Scholar
  9. Chuang, K. and Yang, C .C. 2010. Helping you to help me: Exploring supportive interaction in online health community. In Proceedings of the ASIS&T Annual Meeting. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., and Suri, S. 2008. Feedback effects between similarity and social influence in online communities. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 160--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Domingo, M. C. 2010. Managing Healthcare through Social Networks. Computer 43, 20--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Domingos, P. and Richardson, M. 2001. Mining the network value of customers. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 57--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Domingos, P. and Richardson, M. 2002. Mining knowledge-sharing sites for viral marketing. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Goyal, A., Bonchi, F., and Lakshmanan, L. V. S. 2008. Discovering leaders from community actions. In Proceeding of the 17th ACM Conference on Information and Knowledge Management. ACM, 499--508. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Goyal, A., Bonchi, F., and Lakshmanan, L. V. S. 2010. Learning influence probabilities in social networks. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 241--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jurczyk, P. and Agichtein, E. 2007. Discovering authorities in question answer communities by using link analysis. In Proceedings of the 16th ACM Conference on Information and Knowledge Management. ACM, 919--922. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kempe, D., Kleinberg, J., and Tardos, E. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM New York, NY, 137--146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kimura, M. and Saito, K. 2006. Approximate solutions for the influence maximization problem in a social network. In Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems. B. Gabrys, R. J. Howlett, and L. C. Jain Eds., Springer-Verlag, Berlin, 937--944. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Krulwich, B. and Burkey, C. 1995. ContactFinder: Extracting indications of expertise and answering questions with referrals. In Proceedings of the Symposium on Intelligent Knowledge Navigation and Retrieval. 85--91.Google ScholarGoogle Scholar
  20. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., Vanbriesen, J., and Glance, N. 2007. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Matsuo, Y. and Yamamoto, H. 2009. Community gravity: Measuring bidirectional effects by trust and rating on online social networks. In Proceedings of the 18th International Conference on World Wide Web. ACM New York, NY, 751--760. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Page, L., Brin, S., Motwani, R., and Winograd, T. 1997. PageRank: Bringing order to the Web. www.pcd.stanford.edu/page/papers/pagerank.Google ScholarGoogle Scholar
  23. Schwartz, M. F. and Wood, D. C. M. 1993. Discovering shared interests using graph analysis. ACM 36, 78--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Singla, P. and Richardson, M. 2008. Yes, there is a correlation: From social networks to personal behavior on the Web. In Proceedings of the 17th International Conference on World Wide Web. ACM, 655--664. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Tang, J., Sun, J., Wang, C., and Yang, Z. 2009. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and data mining. ACM New York, NY, 807--816. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Zhang, J., Ackerman, M., and Adamic, L. 2007. Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th International Conference on World Wide Web. ACM New York, NY, 221--230. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 3, Issue 4
      September 2012
      410 pages
      ISSN:2157-6904
      EISSN:2157-6912
      DOI:10.1145/2337542
      Issue’s Table of Contents

      Copyright © 2012 ACM

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      New York, NY, United States

      Publication History

      • Published: 1 September 2012
      • Accepted: 1 October 2011
      • Revised: 1 August 2011
      • Received: 1 December 2010
      Published in tist Volume 3, Issue 4

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