In social network, original publisher and important nodes in the diffusion process can be traced by analyzing the spreading network of a hot topic. The participated users and spreading network structure of a hot topic build an information tracing model, which mines the source and important diffusion nodes. Firstly, it analyzed the development trend of a hot topic and extracts the users involved. Secondly, it established a user network according to the following relationship of the users involved. Thirdly, the contribution rate of users on the development of the hot topic was initialized, and the Page Rank algorithm was used to construct the information tracing model. Finally, the Top k users were selected as the information publisher and important users of the hot topic according to the contribution rate. Experimental results showed that our model can effectively discover the hot topic of the publisher and important users. In social network, original publisher and important nodes in the diffusion process can be traced by analyzing the spreading network of a hot topic. Theicip users and spreading network structure of a hot topic build an information tracing model, which mines the source and important diffusion nodes. Firstly, it analyzed the development trend of a hot topic and extracts the users involved. Secondly, it established a user network according to the following relationship of the users involved. Thirdly, the contribution rate of users on the development of the hot topic was initialized, and the Page Rank algorithm was used to construct the information tracing model. Finally, the Top k users were selected as the information publisher and important users of the hot topic according to the contribution rate. Experimental results showed that our our model can effectively discover the hot topic of the publisher and important users.