With the development of social network, information diffusion problem has received a lot of attention because of its extensive application prospects, and influence maximization problem is a hot topic of information diffusion. Influence maximization aims at selecting nodes that maximize the expected influence as initial nodes of information diffusion, and most work on influence maximization adopts probabilistic models such as independent cascade model. However, most existing solutions of influence maximization view the information diffusion process as an automatic process, and ignore the role of social network websites during the process. Besides, the probabilistic models have some issues in that, for example, they cannot guarantee the information to be delivered effectively, and they cannot adapt the dynamic networks. To tackle the problem, this paper proposes an approach for hot spread node selection based on overlapping community search. This approach selects influence maximized nodes step by step through the iterative promotion model according to users' behavior feedback, and makes the social network websites play the controller's role during information diffusion process. The paper also proposes a new method to measure the influence of nodes based on overlapping community structure, and utilizes this information measure method to select hot spread nodes. Two exact algorithms are proposed including a basic algorithm and an optimized algorithm, as well as an approximate algorithm are presented. Comprehensive experiments demonstrate the performance and accuracy of both exact and approximate algorithms, and the effectiveness of the iterative hot spread node selection method.