Skip to main content
SpringerLink
Log in
Book cover

Knowledge Engineering and Management pp 73–81Cite as

Blog Topic Diffusion Prediction Model Based on Link Information Flow

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 278))

Abstract

How to predict the topic diffusion is a challenging research work in social media data mining. The classical research works in Twitter and Micorblog mainly focus on diffusion links that ignore the importance of diffusion content. In this paper, we propose a Link Information Flow-based topic diffusion prediction model, which combines the link view and content view in diffusion. The experiment results show that our model achieves good performance in topic diffusion prediction.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Gruhl D, Liben-Nowell D et al (2004) Information diffusion through blogspace. SIGKDD Explor Newsl 6(2):43–52

    Article  Google Scholar 

  2. Qamra A, Tseng B et al (2006) Mining blog stories using community-based and temporal clustering. In: Proceedings of the 15th ACM international conference on information and knowledge management. ACM, Arlington, pp 58–67

    Google Scholar 

  3. Yan G, Rizvi S et al (2010) A time-critical information diffusion model in vehicle ad hoc networks. In: Proceedings of the 8th international conference on advances in mobile computing and multimedia. ACM, Paris, pp 102–108

    Google Scholar 

  4. Kuo T-T, Hung S-C, Lin W-S, Peng N, Lin S-D, Lin W-F (2012) Exploiting latent information to predict diffusions of novel topics on social networks. In: Proceedings of the 50th annual meeting of the association for computational linguistics, pp 344–348

    Google Scholar 

  5. Gaussier E (2011) Models of information diffusion in social networks. In: Proceedings of the second symposium on information and communication technology. ACM, Hanoi, pp 2–2

    Google Scholar 

  6. Ishikawa M, Geczy P et al (2007) Information diffusion approach to cold-start problem. In: Proceedings of the 2007 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology—workshops. IEEE Computer Society, pp 129–132

    Google Scholar 

  7. Lee C, Kwak H et al (2010) Finding influentials based on the temporal order of information adoption in twitter. In: Proceedings of the 19th international conference on world wide web. ACM, Raleigh, pp 1137–1138

    Google Scholar 

  8. Leskovec J (2011) Social media analytics: tracking, modeling and predicting the flow of information through networks. In: Proceedings of the 20th international conference companion on world wide web. ACM, Hyderabad, pp 277–278

    Google Scholar 

  9. Lim S-H, Kim S-W et al (2011) Construction of a blog network based on information diffusion. In: Proceedings of the 2011 ACM symposium on applied computing. ACM, TaiChung, pp 937–941

    Google Scholar 

  10. Ratkiewicz J, Conover M et al (2011) Truthy: mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th international conference companion on world wide web. ACM, Hyderabad, pp 249–252

    Google Scholar 

  11. Sambasivan N, Cutrell E et al (2010) ViralVCD: tracing information-diffusion paths with low cost media in developing communities. In: Proceedings of the 28th international conference on human factors in computing systems. ACM, Atlanta, pp 2607–2610

    Google Scholar 

  12. Zinoviev D, Duong V (2011) A game theoretical approach to broadcast information diffusion in social networks. In: Proceedings of the 44th annual simulation symposium. Society for Computer Simulation International, Boston, pp 47–52

    Google Scholar 

  13. Kwon Y-S, Kim S-W et al (2009) The information diffusion model in the blog world. In: Proceedings of the 3rd workshop on social network mining and analysis. ACM, Paris, pp 1–9

    Google Scholar 

  14. Wang D, Li Z et al (2011) The pattern of information diffusion in microblog. In: Proceedings of the ACM CoNEXT student workshop. ACM, Tokyo, pp 1–2

    Google Scholar 

  15. Romero DM, Meeder B et al (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web. ACM, Hyderabad, pp 695–704

    Google Scholar 

  16. Agrawal D, Budak C et al (2011) Information diffusion in social networks: observing and affecting what society cares about. In: Proceedings of the 20th ACM international conference on information and knowledge management. ACM, Glasgow, pp 2609–2610

    Google Scholar 

  17. Kim S-W, Faloutsos C et al (2011) Blogcast effect on information diffusion in a blogosphere. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, Beijing, pp 1149–1150

    Google Scholar 

  18. Kwon Y-S, Kim S-W et al (2009) An analysis of information diffusion in the blog world. In: Proceedings of the 1st ACM international workshop on complex networks meet information knowledge management. ACM, Hong Kong, pp 27–30

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Nature Science Foundation of China (No. 61202143 and No. 61305061), the Natural Science Foundation of Fujian Province (No. 2013J05100, No. 2010J01345 and No. 2011J01367), the Key Projects Fund of Science and Technology in Xiamen (No. 3502Z20123017), the Fundamental Research Funds for the Central Universities (No. 2013121026 and No. 2011121052), the Research Fund for the Doctoral Program of Higher Education of China (No. 201101211120024), the Special Fund for Developing Shenzhen’s Strategic Emerging Industries (No. JCYJ20120614164600201), the Hunan Provincial Natural Science Foundation (12JJ2040), and the Hunan Province Research Foundation of Education Committee(09A046).

Author information

Authors and Affiliations

  1. Cognitive Science Department, Xiamen University, Xiamen, China

    Dazhen Lin & Donglin Cao

Authors
  1. Dazhen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donglin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Donglin Cao .

Editor information

Editors and Affiliations

  1. College of Computer and Software Engineering, Shenzhen University, Shenzhen, China

    Zhenkun Wen

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, D., Cao, D. (2014). Blog Topic Diffusion Prediction Model Based on Link Information Flow. In: Wen, Z., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54930-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-54930-4_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54929-8

  • Online ISBN: 978-3-642-54930-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation