In 2020, the information about Corona Virus Disease 2019 (COVID-19) is overwhelming, which is mixed with a lot of rumors. Rumor and truth can change people’s believes more than once, depending on who is more credible. Here we use credibility to measure the influence one person has on others. Considering costs, we often hope to find the people with the smallest credibility but can achieve the maximum influence. Therefore, we focus on how to use minimal credibility in a given amount of time to clarify rumors. Given the time $t$, the minimum credibility rumor clarifying $\left(MCRC\right)$ problem aims to find a seed set with $k$ users such that the total credibility can be minimized when the total number of the users influenced by positive information reaches a given number at time $t$. In this paper, we propose a Longest-Effective-Hops algorithm called LEH to solve this problem that supposes each user can be influenced two or more times. The theoretical analysis proves that our algorithm is universal and effective. Extensive contrast experiments show that our algorithm is more efficient in both time and performance than the state-of-the art methods.

2020 年，关于 2019 年冠状病毒病 (COVID-19) 的信息铺天盖地，夹杂着大量谣言。谣言和真相可以不止一次改变人们的信仰，这取决于谁更可信。这里我们用可信度来衡量一个人对他人的影响力。考虑到成本，我们往往希望找到信誉度最小但影响力最大的人。因此，我们关注的是如何在给定的时间内以最小的可信度来澄清谣言。鉴于时间$吨$, 最低可信度谣言澄清$\left(米CRC\right)$问题旨在找到一个种子集$k$使得在某时刻受正面信息影响的用户总数达到给定数量时，总可信度可以最小化$吨$. 在本文中，我们提出了一种称为 LEH 的最长有效跳数算法来解决这个假设每个用户可以被影响两次或更多次的问题。理论分析证明我们的算法具有普遍性和有效性。广泛的对比实验表明，我们的算法在时间和性能上都比最先进的方法更有效。