Rumors clarification with minimum credibility in social networks

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 [Formula: see text] , the minimum credibility rumor clarifying [Formula: see text] problem aims to find a seed set with [Formula: see text] 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 [Formula: see text]. 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.