Nodes within a network exhibiting similar interests are grouped as community. Detecting a community in a social network plays a pivotal role in understanding the network’s underlying structure and dynamics. In a community in a social network, rumors can spread misinformation and false narratives, eroding trust and credibility. Detecting and debunking rumors promptly helps maintain the integrity of information shared on social networks. In this study, we aim to identify influential nodes within a social network and analyze the propagation of rumors within the detected community. To assess rumor propagation, we introduce a node-blocking algorithm and based on this model we understand the rate at which a rumor spreads before and after implementing a blocking algorithm. Our experimental findings indicate that our proposed method successfully mitigates the spread of rumors.