Rumors can propagate at great speed through social networks and produce significant damages. In order to control rumor propagation, spreading correct information to counterbalance the effect of the rumor seems more appropriate than simply blocking rumors by censorship or network disruption. In this paper, a competitive diffusion model, namely Linear Threshold model with One Direction state Transition (LT1DT), is proposed for modeling competitive information propagation of two different types in a same network. The problem of minimizing rumor spread in social networks is explored and a novel heuristic based on diffusion dynamics is proposed to solve this problem under the LT1DT. Experimental analysis on four different networks shows that the novel heuristic outperforms pagerank centrality. By seeding correct information in the proximity of rumor seeds, the novel heuristic performs as well as the greedy approach in scale-free and small-world networks but runs three orders of magnitude faster than the greedy approach.

Containment of rumor spread in complex social networks

Li, Zhiwu
;
Giua, Alessandro
Ultimo
2020-01-01

Abstract

Rumors can propagate at great speed through social networks and produce significant damages. In order to control rumor propagation, spreading correct information to counterbalance the effect of the rumor seems more appropriate than simply blocking rumors by censorship or network disruption. In this paper, a competitive diffusion model, namely Linear Threshold model with One Direction state Transition (LT1DT), is proposed for modeling competitive information propagation of two different types in a same network. The problem of minimizing rumor spread in social networks is explored and a novel heuristic based on diffusion dynamics is proposed to solve this problem under the LT1DT. Experimental analysis on four different networks shows that the novel heuristic outperforms pagerank centrality. By seeding correct information in the proximity of rumor seeds, the novel heuristic performs as well as the greedy approach in scale-free and small-world networks but runs three orders of magnitude faster than the greedy approach.
Social networks, Threshold models, Information propagation, Rumor containment.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/285927
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