Online social networks facilitate the spread of information, while rumors can also propagate widely and fast, which may mislead some users. Therefore, suppressing the spread of rumors has become a daunting task. One of the widely used approaches is to select users in the social network to spread the truth and compete against the rumor, so that users who receive the truth before receiving rumors will not trust or propagate the rumor. However, the existing works only aim to speed up blocking rumors without considering the pertinency of users. For example, consider a social media platform operator aiming to enhance user online safety. Based on the user’s online behavior, the users who are at high risk should be alerted first. Motivated by this, we formally define the rumor blocking with pertinence set (RBP) problem, which aims to find a truth seed set that maximizes the number of nodes affected by truth and ensures that the number of influenced nodes within the pertinence set reaches at least a given threshold. To solve this problem, we design a hybrid greedy framework (HGF) algorithm with local and global phases. We prove that HGF can provide a \((1-1/e-\epsilon )\)-approximate solution with high probability while reducing the cost of the sampling process. Extensive experiments on 8 real social networks demonstrate the efficiency and effectiveness of our proposed algorithms.

网络社交网络促进了信息的传播，而谣言也能广泛而快速地传播，这可能会误导一些用户。因此，抑制谣言的传播就成为一项艰巨的任务。广泛采用的做法之一是在社交网络中选择用户来传播真相并与谣言进行竞争，这样在收到谣言之前先了解真相的用户就不会相信或传播谣言。然而，现有的工作只是为了加快封堵谣言的速度，而没有考虑到用户的针对性。例如，考虑一个旨在增强用户在线安全的社交媒体平台运营商。根据用户的上网行为，首先对高风险用户进行预警。受此启发，我们正式定义了相关性集谣言阻塞（RBP）问题，其目的是找到一个真相种子集，使受真相影响的节点数量最大化，并确保相关性集中受影响的节点数量至少达到给定的阈值。为了解决这个问题，我们设计了一种具有局部和全局阶段的混合贪婪框架（HGF）算法。我们证明 HGF 可以以高概率提供\((1-1/e-\epsilon )\)近似解，同时降低采样过程的成本。对 8 个真实社交网络的广泛实验证明了我们提出的算法的效率和有效性。