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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8881)


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.


Social networks Rumor blocking Mathematical programming 


  1. 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. 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)CrossRefGoogle Scholar
  3. 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. 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)CrossRefGoogle Scholar
  5. 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. 6.
    Nguyen, N.P., Yan, G., Thai, M.T.: Analysis of misinformation containment in online social networks. Comput. Netw. 57(10), 2133–2146 (2013)CrossRefGoogle Scholar
  7. 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 2013Google Scholar
  8. 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)CrossRefMATHMathSciNetGoogle Scholar
  9. 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. 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. 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. 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)CrossRefGoogle Scholar
  13. 13.
    Chen, N.: On the approximability of influence in social networks. SIAM J. Discrete Math. 23(3), 1400–1415 (2009)CrossRefMATHMathSciNetGoogle Scholar
  14. 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. 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. 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 2013Google Scholar
  17. 17.
    Erdos, P., Renyi, A.: On the evolution of random graphs. Publ. Math. Inst. Hungary. Acad. Sci. 5, 17–61 (1960)MathSciNetGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  20. 20.
    Agarwal, G., Kempe, D.: Modularity-maximizing graph communities via mathematical programming. Eur. Phys. J. B 66, 409–418 (2008)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Statistical Sciences and Operations ResearchVirginia Commonwealth UniversityRichmondUSA
  2. 2.Department of Computer ScienceVirginia Commonwealth UniversityRichmondUSA

Personalised recommendations