Skip to main content
SpringerLink
Log in

Optimal Containment of Misinformation in Social Media: A Scenario-Based Approach

  • Conference paper
  • First Online:
Combinatorial Optimization and Applications (COCOA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8881))

Abstract

The rapid expanding of online social networks (OSNs) in terms of sizes and user engagements have fundamentally changed the way people communicate and interact nowadays. While OSNs are very beneficial in general, the spread of misinformation or rumors in OSNs not only causes panic in general public but also leads to serious economic and political consequences. Several studies have proposed strategies to limit the spread of misinformation via modifying the topology of the diffusion networks, however, a common limit is that parameters in these diffusion models are difficult, if not impossible, to be extracted from real-world traces. In this paper, we focus on the problem of selecting optimal subset of links whose removal minimizes the spread of misinformation and rumors, relying only on actual cascades that happened in the network. We formulate the link removal problem as a mixed integer programming problem and provide efficient mathematical programming approaches to find exact optimal solutions.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Izadi, E.: Important alert from Chinese state media: No, Ebola isnt a zombie virus, The Washington Post (2014). http://wapo.st/1mRyC09. Accessed 21 August 2014

  2. Kimura, M., Saito, K., Motoda, H.: Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3(2), 9:1–9:23 (2009)

    Article  Google Scholar 

  3. Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, WWW ’11, pp. 665–674. ACM, New York (2011)

    Google Scholar 

  4. Schneider, C.M., Mihaljev, T., Havlin, S., Herrmann, H.J.: Suppressing epidemics with a limited amount of immunization units. Phys. Rev. E 84, 061911 (2011)

    Article  Google Scholar 

  5. He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SDM, pp. 463–474. SIAM (2012)

    Google Scholar 

  6. Nguyen, N.P., Yan, G., Thai, M.T.: Analysis of misinformation containment in online social networks. Comput. Netw. 57(10), 2133–2146 (2013)

    Article  Google Scholar 

  7. Kuhlman, C., Tuli, G., Swarup, S., Marathe, M., Ravi, S.: Blocking simple and complex contagion by edge removal. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 399–408, Dec 2013

    Google Scholar 

  8. Hemmati, M., Cole Smith, J., Thai, M.T.: A cutting-plane algorithm for solving a weighted influence interdiction problem. Comput. Optim. Appl. 57(1), 71–104 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  9. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD’03, pp. 137–146. ACM, New York (2003)

    Google Scholar 

  10. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM ’10, pp. 241–250. ACM, New York (2010)

    Google Scholar 

  11. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: ACM KDD ’07, pp. 420–429. ACM, New York (2007)

    Google Scholar 

  12. Minoux, M.: Accelerated greedy algorithms for maximizing submodular set functions. In: Stoer, J. (ed.) Optimization Techniques. Lecture Notes in Control and Information Sciences, vol. 7, pp. 234–243. Springer, Heidelberg (1978)

    Chapter  Google Scholar 

  13. Chen, N.: On the approximability of influence in social networks. SIAM J. Discrete Math. 23(3), 1400–1415 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  14. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: ACM KDD ’10, pp. 1029–1038. ACM, New York (2010)

    Google Scholar 

  15. Dinh, T., Zhang, H., Nguyen, D., Thai, M.: Cost-effective viral marketing for time-critical campaigns in large-scale social networks. IEEE/ACM Trans. Networking (2014)

    Google Scholar 

  16. Zhang, H., Dinh, T., Thai, M.: Maximizing the spread of positive influence in online social networks. In: 2013 IEEE 33rd International Conference on Distributed Computing Systems (ICDCS), pp. 317–326, July 2013

    Google Scholar 

  17. Erdos, P., Renyi, A.: On the evolution of random graphs. Publ. Math. Inst. Hungary. Acad. Sci. 5, 17–61 (1960)

    MathSciNet  Google Scholar 

  18. Barabasi, A., Albert, R., Jeong, H.: Scale-free characteristics of random networks: the topology of the world-wide web. Physica A 281, 69–77 (2000)

    Article  Google Scholar 

  19. Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  20. Agarwal, G., Kempe, D.: Modularity-maximizing graph communities via mathematical programming. Eur. Phys. J. B 66, 409–418 (2008)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Yongjia Song

  2. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Thang N. Dinh

Authors
  1. Yongjia Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thang N. Dinh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thang N. Dinh .

Editor information

Editors and Affiliations

  1. Zhejiang Normal University, Jinhua, Zhejiang, China

    Zhao Zhang

  2. University of Texas, Tyler, Texas, USA

    Lidong Wu

  3. University of Texas, Dallas, Texas, USA

    Wen Xu

  4. University of Texas, Dallas, USA

    Ding-Zhu Du

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Song, Y., Dinh, T.N. (2014). Optimal Containment of Misinformation in Social Media: A Scenario-Based Approach. In: Zhang, Z., Wu, L., Xu, W., Du, DZ. (eds) Combinatorial Optimization and Applications. COCOA 2014. Lecture Notes in Computer Science(), vol 8881. Springer, Cham. https://doi.org/10.1007/978-3-319-12691-3_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12691-3_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12690-6

  • Online ISBN: 978-3-319-12691-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation