A hot topic diffusion approach based on the independent cascade model and trending search lists in online social networks

In online social networks, users can quickly get hot topic information from trending search lists where publishers and participants may not have neighbor relationships. This paper aims to predict the diffusion trend of a hot topic in networks. For this purpose, this paper first proposes user diffusion willingness, doubt degree, topic contribution, topic popularity and the number of new users. Then, it proposes a hot topic diffusion approach based on the independent cascade (IC) model and trending search lists, named the ICTSL model. The experimental results on three hot topics show that the predictive results of the proposed ICTSL model are consistent with the actual topic data to a great extent. Compared with the IC, independent cascade with propagation background (ICPB), competitive complementary independent cascade diffusion (CCIC) and second-order IC models, the Mean Square Error of the proposed ICTSL model is decreased by approximately 0.78%-3.71% on three real topics.