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
Tripathi, R., Rao, S.: Positive information diffusion for rumor containment in online social networks. In: Proc. COMSNETS, pp. 610–613 (Jan. 2020)
https://www.brandwatch.com/blog/amazing-social-media-statistics-and-facts/, (2019)
Tong, G., Wu, W., Du, D.: Distributed rumor blocking with multiple positive cascades. IEEE Trans. Comput. Soc. Syst. 5(2), 468–480 (2018)
https://foreignpolicy.com/2009/04/25/swine-flu-twitters-power-to-misinform/, (2009)
Zubiaga, A., Ji, H.: Tweet, but verify: epistemic study of information verification on twitter. Soc. Netw. Anal. Min. 4(1), 163 (2014)
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)
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)
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)
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)
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)
Khalil, E., Dilkina, B., Song, L.: Cuttingedge: influence minimization in networks. In: Proc. NIPS Workshop Frontiers Netw. Analysis: Meth., Models, and Appl. NIPS (2013)
Doerr, B., Fouz, M., Friedrich, T.: Why rumors spread so quickly in social networks. Commun. ACM 55(6), 70–75 (2012)
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)
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)
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)
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)
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)
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.
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)
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
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)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proc. ACM SIGKDD. ACM, pp. 199–208 (2009)
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)
Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synth. Lect. Data Manag. 5(4), 1–177 (2013)
W. Chen; C. Castillo; L. V. S. Lakshmanan, Information and Influence Propagation in Social Networks, Morgan & Claypool, 2013.
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI, (2015). [Online]. Available: http://networkrepository.com
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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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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DOI: https://doi.org/10.1007/s41060-021-00293-x