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Rumor containment in peer-to-peer message sharing online social networks

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Abstract

Rumors in online social networks (OSNs) create social chaos, financial losses, and endanger property, which makes rumor containment an important issue. We consider an OSN in which the users communicate via private peer-to-peer messages. We consider the proposed peer-to-peer linear threshold (PLT) and peer-to-peer independent cascade-variant (PICV) models for information diffusion in OSNs, which are variants of the classic IC and LT models, respectively. To combat the rumor spread in the OSN with peer-to-peer message sharing, we employ blocking and positive information diffusion strategies. While in blocking strategy, few users of the OSN called the blocked seed nodes are blocked from spreading the rumor, in positive information diffusion strategy, correct information is introduced into few users of the OSN called positive seed nodes. The positive seed nodes further spread the correct information to other users with time. For a given time-period called the rumor-relevance interval, we determine average number of rumor-influenced nodes for the random, the max-degree, the greedy, the proximity heuristic, and the proposed proximity-weight-degree (PWD)-based containment seed node selection schemes for both blocking and positive information diffusion strategies for PLT and PICV models. We compare the effect of the rumor-relevance interval duration and number of seed nodes on the average number of rumor-influenced nodes for different seed selection algorithms. Our experimental results show that proximity-weight-degree-based seed selection algorithm performs on par with the high-complexity greedy scheme.

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References

  1. Tripathi, R., Rao, S.: Positive information diffusion for rumor containment in online social networks. In: Proc. COMSNETS, pp. 610–613 (Jan. 2020)

  2. https://www.pewresearch.org/fact-tank/2017/11/02/more-americans-are-turning-to-multiple-social-media-sites-for-news/, (2017)

  3. https://www.brandwatch.com/blog/amazing-social-media-statistics-and-facts/, (2019)

  4. Tong, G., Wu, W., Du, D.: Distributed rumor blocking with multiple positive cascades. IEEE Trans. Comput. Soc. Syst. 5(2), 468–480 (2018)

    Article  Google Scholar 

  5. https://foreignpolicy.com/2009/04/25/swine-flu-twitters-power-to-misinform/, (2009)

  6. Zubiaga, A., Ji, H.: Tweet, but verify: epistemic study of information verification on twitter. Soc. Netw. Anal. Min. 4(1), 163 (2014)

    Article  Google Scholar 

  7. 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 

  8. Wang, S., Zhao, X., Chen, Y., Li, Z., Zhang, K., Xia, J.: Negative influence minimizing by blocking nodes in social networks. In: Proc. Workshops 27th AAAI Conf. Artif. Intell. (2013)

  9. Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proc, pp. 665–674. New York, NY, USA, ACM World Wide Web (2011)

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

  11. Wang, B., Chen, G., Fu, L., Song, L., Wang, X.: Drimux: Dynamic rumor influence minimization with user experience in social networks. IEEE Trans. Knowl. Data Eng. 29(10), 2168–2181 (2017)

    Article  Google Scholar 

  12. Khalil, E., Dilkina, B., Song, L.: Cuttingedge: influence minimization in networks. In: Proc. NIPS Workshop Frontiers Netw. Analysis: Meth., Models, and Appl. NIPS (2013)

  13. Doerr, B., Fouz, M., Friedrich, T.: Why rumors spread so quickly in social networks. Commun. ACM 55(6), 70–75 (2012)

    Article  Google Scholar 

  14. Liang, G., He, W., Xu, C., Chen, L., Zeng, J.: Rumor identification in microblogging systems based on users behavior. IEEE Trans. Comput. Social Syst. 2(3), 99–108 (2015)

    Article  Google Scholar 

  15. Luo, W., Tay, W.P., Leng, M.: Infection spreading and source identification: A hide and seek game. IEEE Trans. Signal Process. 64(16), 4228–4243 (2016)

    Article  MathSciNet  Google Scholar 

  16. Guo, J., Chen, T., Wu, W.: A multi-feature diffusion model: rumor blocking in social networks. IEEE/ACM Transactions Netw. 29(1), 386–397 (2021)

    Google Scholar 

  17. Song, C., Hsu, W., Lee, M.L.: Temporal influence blocking: Minimizing the effect of misinformation in social networks. In: Proc. IEEE ICDE, pp. 847–858 (Apr. 2017)

  18. He, Z., Cai, Z., Yu, J., Wang, X., Sun, Y., Li, Y.: Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Trans. Veh. Technol. 66(3), 2789–2800 (2017)

    Article  Google Scholar 

  19. M. A. Manouchehri, M. S. Helfroush and H. Danyali, “Temporal Rumor Blocking in Online Social Networks: A Sampling-Based Approach,” in IEEE Trans. on Syst., Man, and Cybern.: Syst., Early Access (2021), https://doi.org/10.1109/TSMC.2021.3098630.

  20. Tong, G.A., Wu, W., Guo, L., Li, D., Liu, C., Liu, B., Du, D.-Z.: An efficient randomized algorithm for rumor blocking in online social networks. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp. 1–9 (2017)

  21. Yang, L., Li, Z., Giua, A.: Containment of rumor spread in complex social networks. Information Sciences, 506, 113–130 (2020). [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025519306607

  22. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proc, ACM Knowl. Discovery Data Mining. New York, NY, USA, pp. 137–146 (2003)

  23. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proc. ACM SIGKDD. ACM, pp. 199–208 (2009)

  24. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions. Math. Programm. 14(1), 265–294 (1978)

    Article  MathSciNet  Google Scholar 

  25. Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synth. Lect. Data Manag. 5(4), 1–177 (2013)

    Article  Google Scholar 

  26. W. Chen; C. Castillo; L. V. S. Lakshmanan, Information and Influence Propagation in Social Networks, Morgan & Claypool, 2013.

  27. Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI, (2015). [Online]. Available: http://networkrepository.com

  28. Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: In Proc. SODA. SIAM, pp. 946–957 (2014)

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Correspondence to Rohit Tripathi.

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A part of this work is published at International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India, January 2020 [1]

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Tripathi, R., Rao, S. Rumor containment in peer-to-peer message sharing online social networks. Int J Data Sci Anal 13, 185–198 (2022). https://doi.org/10.1007/s41060-021-00293-x

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