Minimize Social Network Rumors Based on Rumor Path Tree

Social networks have become a powerful information spreading platform. How to limit rumor spread on social networks is a challenging problem. In this article, we combine information spreading mechanisms to simulate real-world social network user behavior. Based on this, we estimate the risk degree of each node during the hazard period and analyze the hazard level that other nodes are potentially affected by when a node is infected by a rumor. We use the Rumor Path Tree (
$RPT$
) to analyze the rumor spreading path. By comparing the rumors and truths propagation to a certain node, the steps taken by the rumor node to propagation are estimated. In order to identify the truth node, we construct a fractional function to calculate the effective influence nodes, and select the node with the highest score from the generated
$RPT$
pool. Based on the truth node we effectively block the spread of rumors. Finally, experimental results and comparisons on the real datasets prove that our method is effective and efficient.