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World Wide Web

, Volume 22, Issue 4, pp 1799–1818 | Cite as

Proactive rumor control in online networks

  • Ping Zhang
  • Zhifeng Bao
  • Yudong Niu
  • Yipeng Zhang
  • Songsong Mo
  • Fei Geng
  • Zhiyong PengEmail author
Article
  • 215 Downloads

Abstract

The spread of rumors through online networks not only threatens the public safety but also results in loss of financial property. To serve as a reliable platform for spreading critical information, many work study the problem of rumor control which aims at limiting the pernicious influence of rumors. These methods, however, only assume that users are passive receivers of rumors even if the users can browse the rumors on their own. To overcome this issue, in this paper we study the rumor spread from a proactive perspective and introduce a novel rumor control problem, called users’ B rowsing based rU mor blocK (BUK). Given a rumor set R, BUK can be summarized as targeting k nodes as ‘protectors’ to save nodes in G from being influenced by R as many as possible. Different with the previous studies, BUK considers that the rumors spread via users’ browsing behaviors, and models the propagation based on the random walk model. Theoretical analysis shows that the problem of BUK is submodular, and we propose two greedy algorithms that can approximate BUK within a ratio of (1 − 1/e). However, both of them consume high spaces and thereby cannot be applied to very large networks. Therefore, we further propose a ranking based method RanSel to solve BUK heuristically, which only consumes a linear space to the graph size. The experiments reveal that the effectiveness of our methods outperforms the baseline by 6% to 59.2%, and our methods can achieve such an effective result in reasonable time.

Keywords

Social network Influence spread Rumor control Random walk 

Notes

Acknowledgments

This work is partially supported by the Ministry of Science and Technology of China, National Key Research and Development Program (2016YFB1000700), ARC (DP170102726, DP180102050), NSF of China (Project Number: 61728204, 91646204, 71603252).

References

  1. 1.
    Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378 (2000)CrossRefGoogle Scholar
  2. 2.
    Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–36 (2017)CrossRefGoogle Scholar
  3. 3.
    Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: WINE, pp. 306–311. Springer (2007)Google Scholar
  4. 4.
    Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: SODA, pp. 946–957. SIAM (2014)Google Scholar
  5. 5.
    Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: International Workshop on Internet and Network Economics, pp. 539–550. Springer (2010)Google Scholar
  6. 6.
    Carnes, T., Nagarajan, C., Wild, S.M., Van Zuylen, A.: Maximizing influence in a competitive social network: A follower’s perspective. In: ICEC, pp. 351–360. ACM (2007)Google Scholar
  7. 7.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: SIGKDD, pp. 1029–1038. ACM (2010)Google Scholar
  8. 8.
    Coppersmith, D., Winograd, S.: Matrix multiplication via arithmetic progressions. In: STOC, pp. 1–6. ACM (1987)Google Scholar
  9. 9.
    Cormen, T.H.: Introduction to Algorithms. MIT Press (2009)Google Scholar
  10. 10.
    He, J., Liang, H., Yuan, H.: Controlling infection by blocking nodes and links simultaneously. In: WINE, pp. 206–217. Springer (2011)Google Scholar
  11. 11.
    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
  12. 12.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146. ACM (2003)Google Scholar
  13. 13.
    Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: AAAI, vol. 8, pp. 1175–1180 (2008)Google Scholar
  14. 14.
    Korkmaz, G., Kuhlman, C.J., Ravi, S., Vega-Redondo, F.: Spreading of social contagions without key players. World Wide Web, 1–35 (2017)Google Scholar
  15. 15.
    Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: SIGKDD, pp. 420–429. ACM (2007)Google Scholar
  16. 16.
    Li, H., Bhowmick, S.S., Cui, J., Gao, Y., Ma, J.: Getreal: Towards realistic selection of influence maximization strategies in competitive networks. In: SIGMOD, pp. 1525–1537. ACM (2015)Google Scholar
  17. 17.
    Liu, X., Li, S., Liao, X., Peng, S., Wang, L., Kong, Z.: Know by a handful the whole sack: Efficient sampling for top-k influential user identification in large graphs. World Wide Web 17(4), 627–647 (2014)CrossRefGoogle Scholar
  18. 18.
    Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions—i. Math. Program. 14(1), 265–294 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Newman, M.E., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Phys. Rev. E 66(3), 035101 (2002)CrossRefGoogle Scholar
  20. 20.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the Web. Technical Report (1999)Google Scholar
  21. 21.
    Spitzer, F.: Principles of random walk, vol. 34. Springer Science & Business Media (2013)Google Scholar
  22. 22.
    Tang, Y., Xiao, X., Shi, Y.: Influence maximization: Near-optimal time complexity meets practical efficiency. In: SIGMOD, pp. 75–86. ACM (2014)Google Scholar
  23. 23.
    Tripathy, R.M., Bagchi, A., Mehta, S.: A study of rumor control strategies on social networks. In: CIKM, pp. 1817–1820. ACM (2010)Google Scholar
  24. 24.
    Yan, L., Zheng, W., Zhang, H., Tao, H., He, M.: Learning discriminative sentiment chunk vectors for twitter sentiment analysis 18, 1605–1613 (2017)Google Scholar
  25. 25.
    Yan, L., Yuhui, Z., Cao, J.: Few-shot learning for short text classification.  https://doi.org/10.1007/s11042-018-5772-4 (2018)
  26. 26.
    Yu, Y., Berger-Wolf, T.Y., Saia, J., et al.: Finding spread blockers in dynamic networks. In: ASONAM, pp. 55–76. Springer (2010)Google Scholar
  27. 27.
    Zhang, H., Zhang, H., Li, X., Thai, M.T.: Limiting the spread of misinformation while effectively raising awareness in social networks. In: CSoNet, pp. 35–47. Springer (2015)Google Scholar
  28. 28.
    Zhou, Z., Wang, Y., Wu, Q.M.J., Yang, C.N., Sun, X.: Effective and efficient global context verification for image copy detection. Trans. Info. For. Sec. 12(1), 48–63 (2017).  https://doi.org/10.1109/TIFS.2016.2601065 CrossRefGoogle Scholar
  29. 29.
    Zhou, Z., Wu, Q.J., Huang, F., Sun, X.: Fast and accurate near-duplicate image elimination for visual sensor networks. Int. J. Distrib. Sensor Netw. 13(2), 1550147717694172 (2017).  https://doi.org/10.1177/1550147717694172 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ping Zhang
    • 1
  • Zhifeng Bao
    • 2
  • Yudong Niu
    • 1
  • Yipeng Zhang
    • 2
  • Songsong Mo
    • 1
  • Fei Geng
    • 3
  • Zhiyong Peng
    • 1
    Email author
  1. 1.Wuhan UniversityWuhanChina
  2. 2.RMIT UniversityMelbourneAustralia
  3. 3.Huazhong University of Science and TechnologyWuhanChina

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