Abstract
This paper attempts to address the rumor propagation problem in online social networks (OSNs) and proposes a novel rumor diffusion model, named the HISBmodel. Its originality lies in the consideration of various human factors such as the human social and individual behaviors and the individuals’ opinions. Moreover, we present new metrics that allow accurate assessment of the propagation of rumors. Based on this model, we present a strategy to minimize the influence of the rumor. Instead of blocking nodes, we propose to launch a truth campaign to raise the awareness to prevent the influence of a rumor. This problem is formulated from the perspective of a network inference using the survival theory. The experimental results illustrate that the HISBmodel depicts the evolution of rumor propagation more realistic than classical models. Moreover, Our model highlights the impact of human factors accurately as proven in the studies of the literature. Finally, these experiments showed the outstanding performance of our strategy to minimize the influence of the rumor by selecting precisely the candidate nodes to diminish the influence of the rumor.
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References
Afassinou, K.: Analysis of the impact of education rate on the rumor spreading mechanism. Phys. A Stat. Mech. Appl. 414, 43–52 (2014)
Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 539–550. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17572-5_48
Bredereck, R., Elkind, E.: Manipulating opinion diffusion in social networks. In: Proceedings of the 26th IJCAI (2017)
Budak, C., Abbadi, A.E.: Limiting the spread of misinformation in social networks. Distribution, pp. 665–674 (2011)
Daley, D., Kendall, D.: Epidemics and rumours. Nature 204(4963), 1118 (1964)
DiFonzo, N., Bordia, P., Rosnow, R.L.: Reining in rumors. Organ. Dyn. 23(1), 47–62 (1994)
Fan, L., Lu, Z., Wu, W., Bhavani, T., Ma, H., Bi, Y.: Least cost rumor blocking in social networks. In: IEEE 33rd International Conference on Distributor Computer Systems (2013)
Galam, S.: Modelling rumors: the no plane pentagon french hoax case. Phys. A Stat. Mech. Appl. 320(7603), 571–580 (2003)
Gomez-Rodriguez, M., Leskovec, J.: Modeling information propagation with survival theory. In: Proceedings of the 30th ICML (ICML-13), pp. 666–674 (2013)
Han, S., Zhuang, F., He, Q., Shi, Z., Ao, X.: Energy model for rumor propagation on social networks. Phys. A Stat. Mech. Appl. 394, 99–109 (2014)
He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. Education p. Technical report CoRR abs/1110.4723 (2011)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, p. 137 (2003)
Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: AAAI, pp. 1175–1180 (2008)
Leskovec, J., Krevl, A.: SNAP datasets: stanford large network dataset collection, June 2014
Ma, J., Li, D., Tian, Z.: Rumor spreading in online social networks by considering the bipolar social reinforcement. Phys. A: Stat. Mech. Appl. 447, 108–115 (2016)
Marion, J.B., S.T.T.: Classical dynamics of particles and systems. Thomson (2003)
Meshi, D., Morawetz, C., Heekeren, H.R.: Nucleus accumbens response to gains in reputation for the self relative to gains for others predicts social media use. Front. Hum. Neurosci. 7, 439 (2013)
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)
Wang, H., Deng, L., Xie, F., Xu, H., Han, J.: A new rumor propagation model on SNS structure. In: Proceedings of IEEE International Conference Granular Computing GrC 2012 (2012)
Wang, J., Wang, Y.Q., Li, M.: Rumor spreading considering the herd mentality mechanism. In: Control Conference, 2017 36th Chinese, pp. 1480–1485. IEEE (2017)
Wang, Y.Q., Yang, X.Y., Han, Y.L.: Rumor spreading model with trust mechanism in complex social networks. Commun. Theor. Phys. 59(4), 510 (2013)
Xia, L.L., Jiang, G.P., Song, B., Song, Y.R.: Rumor spreading model considering hesitating mechanism in complex social networks. Phys. A Stat. Mech. Appl. 437, 295–303 (2015)
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. Time 468, 177–186 (2011)
Zhao, L., Cui, H., Qiu, X., Wang, X., Wang, J.: SIR rumor spreading model in the new media age. Phys. A Stat. Mech. Appl. 392(4), 995–1003 (2013)
Zhao, L., Wang, J., Chen, Y., Wang, Q., Cheng, J., Cui, H.: SIHR rumor spreading model in social networks. Phys. A Stat. Mech. Appl. 391(7), 2444–2453 (2012)
Zhao, L., Wang, Q., Cheng, J., Chen, Y., Wang, J., Huang, W.: Rumor spreading model with consideration of forgetting mechanism: A case of online blogging LiveJournal. Phys. A Stat. Mech. Appl. 390(13), 2619–2625 (2011)
Zubiaga, A., Hoi, G.W.S., Liakata, M., Procter, R.: Pheme dataset of rumours and non-rumours. (2016)
Acknowledgment
The Research was supported in part by National Basic Research Program of China (973 Program, No.2013CB329605).
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Hosni, A.I.E., Li, K., Ahmed, S. (2018). HISBmodel: A Rumor Diffusion Model Based on Human Individual and Social Behaviors in Online Social Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_2
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DOI: https://doi.org/10.1007/978-3-030-04179-3_2
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