Targeted Influence Maximization under a Multifactor-Based Information Propagation Model

•We propose a new multifactor-based information propagation model (MFIP), which considers both the information content and user characteristics. The proposed propagation model can assist in spreading information to the targeted users.•Under the proposed MFIP, to maximize the information influence on the target audiences, we propose the WDD seed nodes selection heuristic algorithm. The WDD algorithm uses an alternative metric to approximate the influence of nodes.•Extensive experiments are conducted in four social networks, and the results demonstrate that our MFIP model performs better than the comparison models; moreover, the WDD seed selection algorithm has high efficiency and effectiveness.