Skip to main content
SpringerLink
Log in

Rumor Blocking in Social Networks

  • Chapter
  • First Online:
Optimal Social Influence

Part of the book series: SpringerBriefs in Optimization ((BRIEFSOPTI))

  • 648 Accesses

Abstract

Online social networks have many benefits as a medium for fast, widespread information dissemination. They provide fast access to large-scale news data, sometimes even before the mass media. They also serve as a medium to collectively achieve a social goal. For instance with the use of group and event pages in Facebook, events such as Day of Action protests reached thousands of protestors. While the ease of information propagation in social networks can be very beneficial, it can also have disruptive effects. One such example was observed in August 2012, thousands of people in Ghazni province left their houses in the middle of the night in panic after the rumor of earthquake. Another example is the fast spread of misinformation in twitter that the president of Syria is dead, leading to a sharp, quick increase in the price of oil. There are lots of similar examples. Although social networks are the main source of news for many people today, they are not considered reliable due to such problems.

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. N. Alon, M. Feldman, A.D. Procaccia, M. Tennenholtz, A note on competitive diffusion through social networks. Inf. Process. Lett. 110(6), 221–225 (2010)

    Article  MathSciNet  Google Scholar 

  2. S. Bharathi, D. Kempe, M. Salek, Competitive influence maximization in social networks. Internet Netw. Econ. 4858, 306–311 (2007)

    Article  Google Scholar 

  3. V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of community hierarchies in large networks. J. Stat. Mech. 2008, P10008 (2008)

    Article  Google Scholar 

  4. A. Borodin, Y. Filmus, J. Oren, Threshold models for competitive influence in social networks, in WINE (2010), pp. 539–550

    Google Scholar 

  5. C. Budak, D. Agrawal, A.E. Abbadi, Limiting the spread of misinformation in social networks. WWW (2011), pp. 665–674

    Google Scholar 

  6. T. Carnes, C. Nagarajan, S.M. Wild, A.V. Zuylen, Maximizing influence in a competitive social network: a follower’s perspective, in Proceedings of the 9th International Conference on Electronic Commerce (ICEC) (2007), pp. 351–360

    Google Scholar 

  7. W. Chen, Y. Wang, S. Yang, Efficient influence maximization in social networks, in KDD (2009), pp. 199–208

    Google Scholar 

  8. W. Chen, Y. Yuan, L. Zhang, Scalable influence maximization in social networks under the linear threshold model, in ICDM (2010), pp. 88–97

    Google Scholar 

  9. L. Fan, Z. Lu, W. Wu, B.M. Thuraisingham, H. Ma, Y. Bi, Least cost rumor blocking in social networks, in ICDCS (2013), pp. 540–549

    Google Scholar 

  10. L. Fan, W. Wu, X. Zhai, K. Xing, W. Lee, D.Z. Du, Maximizing rumor containment in social networks with constrained time. Soc. Netw. Anal. Min. 4(1), 214 (2014)

    Google Scholar 

  11. U. Feige, A threshold of ln n for approximating set cover. J. ACM 45(4), 634–652 (1998)

    Article  MathSciNet  Google Scholar 

  12. J. Garrison, C. Knoll, Prop. 8 opponents rally across california to protest gay-marriage ban. Los Angeles Times (2008, November). http://www.latimes.com/local/la-me-prop816-2008nov16-story.html#page=1.html

  13. X. He, G. Song, W. Chen, Q. Jiang, Influence blocking maximization in social networks under the competitive linear threshold model, in SDM (2012)

    Google Scholar 

  14. D. Kempe, J. Kleinberg, E. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2003), pp. 137–146

    Google Scholar 

  15. M. Kimura, K. Saito, Tractable models for information diffusion in social networks, in Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (2006), pp. 259–271

    Google Scholar 

  16. M. Kimura, K. Saito, R. Nakano, Extracting influential nodes for information diffusion on a social network, in AAAI (AAAI Press, Palo Alto, 2007), pp. 1371–1376

    Google Scholar 

  17. M. Kimura, K. Saito, H. Motoda, Minimizing the spread of contamination by blocking links in a network, in Proceedings of the 23rd AAAI Conference on Artificial Intelligence (2008)

    Google Scholar 

  18. B. Klimt, Y. Yang, Introducing the Enron corpus, in CEAS (2004)

    MATH  Google Scholar 

  19. J. Kostka, Y.A. Oswald, R. Wattenhofer, Word of mouth: Rumor dissemination in social networks, in SIROCCO (2008), pp. 185–196

    Google Scholar 

  20. A. Lancichinetti, S. Fortunato, Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 056117 (2009)

    Article  Google Scholar 

  21. J. Leskovec, J.M. Kleinberg, C. Faloutsos, Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1), 2 (2007)

    Article  Google Scholar 

  22. J. Leskovec, L. Backstrom, J. Kleinberg, Meme-tracking and the dynamics of the news cycle, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2009), pp. 497–506

    Google Scholar 

  23. Z. Lu, W. Zhang, W. Wu, B. Fu, D.-Z. Du, Approximation and inapproximation for the influence maximization problem in social networks under deterministic linear threshold model, in ICDCS Workshops (IEEE Computer Society, Washington DC, 2011), pp. 160–165

    Google Scholar 

  24. S. Maftoon, Quake rumour sends thousands into Ghazni streets (2012). http://www.pajhwok.com/en/2012/08/20/quake-rumour-sends-thousands-ghazni-streets

    Google Scholar 

  25. R. Narayanam, Y. Narahari, Determining the top-k nodes in social networks using the shapley value, in Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (2008), pp. 1509–1512

    Google Scholar 

  26. G.L. Nemhauser, L.A. Wolsey, M. Fisher, An analysis of the approximations for maximizing submodular set functions. Math. Program. 14, 265–294 (1978)

    Article  MathSciNet  Google Scholar 

  27. M.E.J. Newman, The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. (PNAS) 98, 409–415 (2001)

    MathSciNet  MATH  Google Scholar 

  28. N.P. Nguyen, G. Yan, M.T. Thai, S. Eidenbenz, Containment of misinformation spread in online social networks, in Proceedings of the 4th Annual ACM Web Science Conference (WebSci) (2012), pp. 213–222

    Google Scholar 

  29. D. Trpevski, W.K.S. Tang, L. Kocarev, Model for rumor spreading over networks. Phys. Rev. E 81, 056102 (2010)

    Article  Google Scholar 

  30. J. Tsai, T.H. Nguyen, M. Tambe, Security games for controlling contagion, in AAAI, ed. by J. Hoffmann, B. Selman (AAAI Press, Palo Alto, 2012)

    Google Scholar 

  31. J. Tsai, Y. Qian, Y. Vorobeychik, C. Kiekintveld, M. Tambe, Bayesian security games for controlling contagion, in SocialCom (IEEE, Piscataway, 2013), pp. 33–38

    Google Scholar 

  32. V. Tzoumas, C. Amanatidis, E. Markakis, A game-theoretic analysis of a competitive diffusion process over social networks, in WINE, ed. by P.W. Goldberg (2012), pp. 1–14

    Google Scholar 

  33. Y. Wang, G. Cong, G. Song, K. Xie, Community-based greedy algorithm for mining top-k influential nodes in mobile social networks, in KDD (2010), pp. 1039–1048

    Google Scholar 

  34. W. Xu, Z. Lu, W. Wu, Z. Chen, A novel approach to online social influence maximization. J. Soc. Netw. Anal. Min. (SNAM) 4(1), 153–164 (2014)

    Google Scholar 

  35. Yahoo News, Twitter rumor leads to sharp increase in the price of oil (2012). http://news.yahoo.com/blogs/technology-blog/twitter-rumor-leads-sharp-increase-price-oil-173027289.html

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Texas Woman’s University, Denton, TX, USA

    Wen Xu

  2. Department of Computer Science, University of Texas, Dallas, Richardson, TX, USA

    Weili Wu

Authors
  1. Wen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weili Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xu, W., Wu, W. (2020). Rumor Blocking in Social Networks. In: Optimal Social Influence. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-37775-5_4

Download citation

Publish with us

Policies and ethics

Search

Navigation