Simulation Research on Network News Information Dissemination Model Based on Data Mining Algorithm
Lei Hu · 2023
News is widely forwarded and commented on the Internet, and constantly updated, which hides unfavorable remarks, which have an important impact on the security of cyberspace and the stability of the real society. In this paper, a network news information dissemination model based on data mining algorithm is established, and a parallel adaptive topic tracking algorithm based on N-Gram is designed by combining the advantages of NB(naive Bayesian) classification algorithm and both, which can improve the topic tracking accuracy and improve the topic drift phenomenon. Calculate the attribute credibility of news reports belonging to each news topic, select the news reports with greater than the minimum attribute credibility to update the training set, and update the topic model to complete the news topic tracking. Simulation results show that the important users identified by this model are more active than those obtained by other methods. As the number of nodes increases, the speedup ratio of the algorithm also increases, which proves that the algorithm in this paper has strong scalability.