A Novel Approach to Limit the Spread of Wrong Information in Social Networks

Authors(2) :-K. Ravikumar, N. Sindhuja

In this effort, we study the idea of competing campaigns in a social network. By demonstrating the spread of effect in the presence of competing campaigns, we provide necessary tools for applications such as emergency response where the goal is to limit the spread of misinformation. We revisited the problematic of effect limitation where a bad campaign twitches spreading from a certain node in the grid and use the notion of limiting campaigns to counteract the misinformation. The problem can be summarized as identifying a subset of folks that need to be convinced to adopt the competing (or \decent") movement so as to minimalize the number of people that adopt the \bad" campaign at the end of both propagation processes. We demonstration that this optimization problematic is NP-hard and deliver estimate assurances for an avaricious answer for various meanings of this problem by proving that they are submodular. Though the greedy algorithm is a polynomial time algorithm, for today's big scale social networks even this answer is computation-ally actual expensive. Consequently, we study the presentation of the degree importance experiential as well as other heuristics that have insinuations on our exact problem. The experiments on a number of close-knit regional networks obtained from the Facebook social network show that in most belongings inexpensive heuristics do in fact compare well with the greedy approach.

Authors and Affiliations

K. Ravikumar
Assistant. professor Department of Computer Science, Tamil University, Thanjavur, Tamil Nadu, India
N. Sindhuja
Research Scholar, Department of Computer Science, Tamil University, Thanjavur, Tamil Nadu, India

Social Systems, Evidence Cascades, Misrepresentation Proliferation, Competing Movements, Submodular Occupations

  1. R. M. Anderson and R. M. May. Infectious Diseases of Humans: Dynamics and Control (Oxford Science Publications). Oxford University Press, USA, new ed edition, September 1992.
  2. N. T. J. Bailey. The mathematical theory of infectious diseases and its applications / [byNorman T. J. Bailey. Gri n, London :, 2nd ed. edition, 1975.
  3. S. Bharathi, D. Kempe, and M. Salek. Competitive in uence maximization in social networks. In In WINE, pages 306{311, 2007.
  4. T. Carnes, C. Nagarajan, S. M. Wild, and A. van Zuylen. Maximizing in uence in a competitive social network: a follower's perspective. In ICEC '07: Proceedings of the ninth international conference on Electronic commerce, pages 351{360, New York, NY, USA, 2007. ACM.
  5. W. Chen, Y.Wang, and S. Yang. E cient in uence maximization in social networks. In Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, pages 199{208, 2009.
  6. G. Cornuejols, M. L. Fisher, and G. L. Nemhauser. Location of bank accounts to optimize oat: An analytic study of exact and approximate algorithms. Management Science, 23(8):789{810, 1977.
  7. O. Diekmann and J. A. P. Heesterbeek. Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation (Wiley Series in Mathematical & Computational Biology). Wiley, 1 edition, May 2000.
  8. P. Domingos and M. Richardson. Mining the network value of customers. In Proceedings of the 7th ACM International Conference on Knowledge Discovery and Data Mining, pages 57{66, 2001.
  9. P. Dubey, R. Garg, and B. D. Meyer. Competing for customers in a social network. Cowles Foundation Discussion Papers 1591, Cowles Foundation, Yale University, Nov. 2006.
  10. R. Durrett. Lecture notes on particle systems and percolation. Wadsworth Publishing, 1988.
  11. Facebook. http://www.facebook.com.
  12. J. Garrison and C. Knoll. Prop. 8 opponents rally across california to protest gay-marriage ban. Los Angeles Times, November 2008.
  13. J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 2001.
  14. M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, 1978.
  15. H. W. Hethcote. The mathematics of infectious diseases. SIAM Rev., 42(4):599{653, 2000.
  16. K. M. Heussner. Ft. hood soldier causes stir on twitter. ABS News (online), November 2009.
  17. D. Kempe, J. M. Kleinberg, and E. Tardos. Maximizing the spread of in uence through a social network. In Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining, pages 137{146, 2003.
  18. J. O. Kephart and S. R. White. Measuring and modeling computer virus prevalence. In SP '93: Proceedings of the 1993 IEEE Symposium on Security and Privacy, page 2, Washington, DC, USA, 1993. IEEE Computer Society.
  19. M. Kimura, K. Saito, R. Nakano, and H. Motoda. Social Computing and Behavioral Modeling, chapter Finding In uential Nodes in a Social Network from Information Di usion Data. Springer US, 2009.
  20. M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse. Identifying in uential spreaders in complex networks. Jan 2010.

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
Manuscript Number : CSEIT1831136
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Ravikumar, N. Sindhuja, "A Novel Approach to Limit the Spread of Wrong Information in Social Networks", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp., January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831136

Article Preview