Advertisement

Hot Topic Detection in News Blog Based on W2T Methodology

  • Erzhong Zhou
  • Ning ZhongEmail author
  • Yuefeng Li
  • Jiajin Huang
Chapter
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)

Abstract

A social event is often unlimitedly amplified and promptly spread in blogspace, and it is valuable to correctly detect blog hot topics for managing the cyberspace. Although hot topic detection techniques have a great improvement, it is more significant o find what determines the life span of a blog topic, because the online consensus brought by the topic unavoidably experiences the real life. The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks. This chapter first applies the W2T methodology to analyze the formation and evolution of a blog hot topic, and some influential factors which determine the development of the topic are identified to recognize hot topics. And then, the construction of a blog topic model considers information granularity in order to detect and track the evolution of the topic. Experimental results show that the proposed method for detecting the blog hot topic is feasible and effective.

Notes

Acknowledgments

The study was supported by National Natural Science Foundation of China (61272345).

References

  1. 1.
    L. Akritidis, D. Katsaros, P. Bozanis, Identifying the productive and influential bloggers in a community. IEEE Trans. Syst. Man Cybern. 41(5), 759–764 (2011)CrossRefGoogle Scholar
  2. 2.
    K. Andreas, A. Henning, S. Varinder, Social activity and structural centrality in online social networks. Telematics Inform. 32(2), 321–332 (2015)CrossRefGoogle Scholar
  3. 3.
    E. Bakshy, B. Karrer, B.L.A. Adamic, Social influence and the diffusion of user-created content, in Proceedings of the 2009 ACM Conference on Electronic Commerce (2009), pp. 325–334Google Scholar
  4. 4.
    N. Bansal, F. Chiang, N. Koudas, et al., Seeking stable clusters in the blogosphere, in Proceedings of the Thirty-Third International Conference on Very Large Data Bases (2007), pp. 806–817Google Scholar
  5. 5.
    F. Bodendorf, C. Kaiser, Detecting opinion leaders and trends in online social networks, in Proceedings of the Fourth International Conference on Digital Society (2010), pp. 124–129Google Scholar
  6. 6.
    Y.Z. Cao, P.J. Shao, L.Q. Li, Topic propagation model based on diffusion threshold in blog networks, in Proceedings of 2011 International Conference on Business Computing and Global Information (2011), pp. 539–542Google Scholar
  7. 7.
    K.Y. Chen, L. Luesukprasert, S.C.T. Chou, Hot topic extraction based on timeline analysis and multidimensional sentence modeling. IEEE Trans. Knowl. Data Eng. 19(8), 1016–1025 (2007)CrossRefGoogle Scholar
  8. 8.
    C.C. Chen, Y.T. Chen, M.C. Chen, An aging theory for event life-cycle modeling. IEEE Trans. Syst. Man Cybern. 37(2), 237–248 (2007)CrossRefGoogle Scholar
  9. 9.
    X.Y. Dai, Q.C. Chen, X.L. Wang et al., Online topic detection and tracking of financial news based on hierarchical clustering, in Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, vol. 6 (2010), pp. 3341–3346Google Scholar
  10. 10.
    M. Gomez-Rodriguez, J. Leskovec, A. Krause, Inferring networks of diffusion and influence, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data (2010), pp. 1019–1028Google Scholar
  11. 11.
    H.J. Gong, Research on Automatic Network Hot Topics Detection (Central china normal university, Wuhan, 2008)Google Scholar
  12. 12.
    T.T. He, G.Z. Qu, S.W. Li, et al., Semi-automatic hot event detection, in Proceedings of the Second International Conference on Advanced Data Mining and Applications (2006), pp. 1008–1016Google Scholar
  13. 13.
    H.H. Huang, Y.H. Kuo, Cross-lingual document representation and semantic similarity measure a fuzzy set and rough set based approach. IEEE Trans. Fuzzy Syst. 18(6), 1098–1111 (2010)CrossRefGoogle Scholar
  14. 14.
    ICTCLAS. Home page: http://ictclas.org/
  15. 15.
    H. Li, J.F. Wei, Netnews bursty hot topic detection based on burtsy features, in Proceedings of the International Conference on e-Business and e-Government (2010), pp. 1437–1440Google Scholar
  16. 16.
    N. Li, D.D. Wu, Using text mining and sentimen analysis for online forums hotspot detection and forecast. Decis. Support Syst. 48(2), 354–368 (2010)CrossRefGoogle Scholar
  17. 17.
    S.H. Lim, S.W. Kim, S.J. Park, J.H. Lee, Determining content power users in a blog network: an approach and its applications. IEEE Trans. Syst. Man Cybern. 41(5), 853–862 (2011)CrossRefGoogle Scholar
  18. 18.
    S.A. Myers, C.G. Zhu, J. Leskovec, Information diffusion and external influence in networks, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012), pp. 33–41Google Scholar
  19. 19.
    H.M. Qiu, The Social Network Analysis of Blogosphere (Harbin institute of technology, Harbin, 2007)Google Scholar
  20. 20.
    G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  21. 21.
    Sina Blog Website. Home page: http://blog.sina.com.cn/
  22. 22.
    Sogou Laboratory. Home page: http://www.sogou.com/labs/dl/c.html
  23. 23.
    W.J. Sun, H.M. Qiu, A social network analysis on blogospheres, in Proceedings of 2008 International Conference on Management Science and Engineering (2008), pp. 1769–1773Google Scholar
  24. 24.
    J.H. Wang, Web-based verification on the representativeness of terms extracted from single short documents, in Proceedings of 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 3 (2011), pp. 114–117Google Scholar
  25. 25.
    G.H. Xie, The Research on the System of the Affect of Internet Opinion Leaders (Central China normal university, Wuhan, 2011)Google Scholar
  26. 26.
    J.J. Yao, B. Cui, Y.X. Huang, Bursty event detection from collaborative tags. World Wide Web 15(2), 171–195 (2012)CrossRefGoogle Scholar
  27. 27.
    H. Yu, Research on the Opinion Leaders of Political BBS: An Case Study on Sino-Japan BBS of Strong Nation Forum (Huazhong university of science and technology, Wuhan, 2007)Google Scholar
  28. 28.
    Z.F. Zhang, Q.D. Li, QuestionHolic: hot topic discovery and trend analysis in community question answering sytems. Expert Syst. Appl. 38(6), 6848–6855 (2011)CrossRefGoogle Scholar
  29. 29.
    N. Zhong, J.H. Ma, R.H. Huang et al., Research challenges and perspectives on Wisdom Web of Things (W2T). J. Supercomput. 64(3), 862–882 (2010)CrossRefGoogle Scholar
  30. 30.
    N. Zhong, J.M. Bradshaw, J.M. Liu et al., Brain informatics. IEEE Intell. Syst. 26(5), 16–21 (2011)CrossRefGoogle Scholar
  31. 31.
    E.Z. Zhou, N. Ning, Y.F. Li, Extracting news blog hot topics based on the W2T methodology. World Wide Web (2014). doi: 10.1007/s11280-013-0207-7

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Erzhong Zhou
    • 1
  • Ning Zhong
    • 2
    • 3
    Email author
  • Yuefeng Li
    • 4
  • Jiajin Huang
    • 1
  1. 1.International WIC Institute, Beijing University of TechnologyBeijingChina
  2. 2.Beijing Advanced Innovation Center for Future Internet Technology, The International WIC InstituteBeijing University of TechnologyBeijingChina
  3. 3.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan
  4. 4.Faculty of Science and Technology, Queensland University of TechnologyBrisbaneAustralia

Personalised recommendations