Online social networks (OSNs) connect people around the globe under one virtual society. It helps people gather, communate and share their common interests. But many times, OSNs are also exploited and eventually become a major platform for rumor or false information propagation. Controlling such rumors in OSNs has been the most challenging research interest in recent days. Since OSNs are a platform of collective behavior, we focus on a collective rumor containment approach to control or eradicate rumors. In this paper, an anti-rumor information spreading approach is proposed to contain rumors collectively by following a bio-inspired immunization method called social immunity. First, A competitive information propagation model called competitive cascade (CC) model that spreads rumor and true information simultaneously is defined. This model continuously updates the trustworthiness of individuals in the network on every communication among the participants of OSNs. Then, the initial spreaders of anti-rumors are identified with the help of the intensity of the rumor in the network as well as the individual’s trustworthiness. Finally, a collective rumor containment approach is applied by considering the cost of rumor containment and a rumor intensity threshold. The proposed approach is compared with recent and well-known rumor control approaches and the results show that the proposed approach is effective in eradicating rumors.

在线社交网络（OSN）将一个虚拟社会下的全球人们联系起来。它可以帮助人们聚集，交流和分享共同的利益。但是很多时候，OSN也被利用，并最终成为谣言或虚假信息传播的主要平台。近年来，控制OSN中的此类谣言一直是最具挑战性的研究兴趣。由于OSN是集体行为的平台，因此我们专注于采用集体谣言遏制方法来控制或消除谣言。本文提出了一种反谣言信息传播方法，通过遵循一种称为社会免疫​​的生物启发免疫方法来集体遏制谣言。首先，定义了一种竞争信息传播模型，称为竞争级联（CC）模型，该模型同时传播谣言和真实信息。该模型在OSN参与者之间的每次通信中不断更新网络中个人的信任度。然后，借助网络中谣言的强度以及个人的信任度来确定反谣言的最初传播者。最后，通过考虑谣言遏制的成本和谣言强度阈值来应用集体谣言遏制方法。将提出的方法与最新的和众所周知的谣言控制方法进行了比较，结果表明，该方法在消除谣言方面是有效的。借助网络中谣言的强度以及个人的信任度来确定反谣言的最初传播者。最后，通过考虑谣言遏制的成本和谣言强度阈值来应用集体谣言遏制方法。将提出的方法与最新的和众所周知的谣言控制方法进行了比较，结果表明，该方法在消除谣言方面是有效的。借助网络中谣言的强度以及个人的信任度来确定反谣言的最初传播者。最后，通过考虑谣言遏制的成本和谣言强度阈值来应用集体谣言遏制方法。将提出的方法与最新的和众所周知的谣言控制方法进行了比较，结果表明，该方法在消除谣言方面是有效的。