In order to rapidly detect the information dissemination ways and alleviate the influence of malicious information, a user Content and Structure-based Influence Algorithm with Iteration (CSIAI) was proposed. The word-user documentation similarity was iteratively computed by the proposed algorithm through the content modeling of user's microblog. Through the concern and attention behaviors of microblog, user relational structures were established and user influence weights were calculated to get the adjacency matrix of user influence. The k nodes with higher influence were extracted as the information transmission path. In the detection simulation experiments, the influence coverage rate and response time were adopted as the evaluation indexes, According to the expansion of the new knowledge base, the relationships of parameters α and β of CSIAI were determined based on the extended new knowledge base. With the increase of users, the influence coverage rate and response time performance of the proposed CSIAI are superior to the algorithms of PageRank, CELF and Content and Structure-based Influence Algorithm (CSIA) without iteration. The experimental results show that the proposed CSIAI can effectively detect the dissemination of microblog information.

Aiming at the deficiency of traditional text representation model, which usually ignores term correlation, and topic drifting problem during topic tracking, this paper propose an approach called self-adaptive microblog hot topic tracking method using terms correlation. Mutual information between terms in the same and different microblogs are investigated. Then the conventional text representation model is updated. Similarity calculation is performed to decide whether it is the subsequent discussions of a certain hot topic. Finally, the vectors of microblogs are updated to avoid topic drifting. Experiments show the effectiveness of our method.