Due to the diverse structure and varying sizes of networks, identifying super spreaders to limit the spread of information (or infection) has become a significant research challenge. Community-based approaches have shown effectiveness in locating spreaders across diverse network positions. To address gaps in current methods, we propose Community-based Closeness Coreness (CCC), a novel approach for controlling spreading dynamics. CCC first identifies gateway nodes within each community and calculates each node’s closeness with these gateway nodes within a truncation radius, reflecting its spreading efficiency. Node coreness is measured by combining the k-shell index and neighbor connectivity, and nodes are ranked based on their aggregated closeness and coreness. Finally, CCC selects spreaders from all communities according to node ranking and community size. Measuring the performance of CCC on six real networks shows that it outperforms baseline methods by identifying super spreaders that enable rapid and extensive diffusion.