Aiming to optimize marketing promotion, an information dissemination model integrating trust and user preference is developed. The objective is to capture users’ behavioral mechanisms and enhance marketing decision-making efficiency. To measure users’ trust in key opinion leaders, an Interval Type-2 Fuzzy Sets (IT2FSs) -based trust evaluation model is created, enabling effective trust assessment and stimulating purchasing behavior. Regarding the dynamic nature of user preferences, a Hidden Markov Model (HMM) -based prediction algorithm is proposed to track interest changes and forecast repurchase behavior. Considering rational and irrational user behaviors in marketing information dissemination, two new states, repurchase state P and hesitant purchasing state H are introduced based on the Susceptible-Infected-Recovered (SIR) model. Then, the SIRPH social platform information dissemination model is constructed, achieving accurate prediction and enhancement of marketing information dissemination. Experimental results indicate that the SIRPH model reduces the peak purchasing users by 15–25%, extends topic lifecycles by 20–30%, and improves information spreading accuracy, demonstrating the effectiveness of trust and preference integration.

The propagation of hot topics often gives rise to a series of derivative topics. In view of the sparsity of user behavior data and the cognitive accumulation of the original topic, a prediction model of derived topic propagation based on cognitive accumulation and transfer learning is proposed. First, for the complexity of the derived topic feature space, considering the relation and difference between derivative topics and original topics, this study designs I(Iterative)T(Topic)2vec, a topic iterative representation method based on original topics to get the low-dimensional representation of the derived topic feature space more richly from the perspectives of both original topics and derivative topics. Second, it aims at the problem of users’ cognitive accumulation of the original topic before the outbreak of derivative topic. The subjective game theory is introduced to construct the cognitive influence of users. At the same time, considering the timeliness of the propagation cycle of derivative topics, we discretized the derivative topic data, and further proposed a derivative topic propagation model based on Subjective Adapt-CNN (SA-CNN). Finally, the sparsity of effective behavior data of users at the beginning of the outbreak of derivative topics is discussed. Considering the rich user behavior data in the communication history of the original topic, data migration is carried out by using the original topic. At the same time, the domain adaptive method based on Transfer Component Analysis (TCA) is introduced to achieve feature adaptation from the original topic data to the derived topic data, further improving the accuracy of the derived topic propagation model. Experiments show that this model can not only effectively alleviate the problem of data sparsity but also perceive the propagation situation of derived topics well.

Discovering hidden key users of leading topics plays an important role in opinion control and risk prevention. Aiming at the dynamic nature of key users’ intentions and other problems, a key user discovery model based on behavioral intentions and implicit relationships is proposed. First, to address the dynamic nature of key users’ intentions, the dynamic latent Dirichlet allocation method is introduced. This approach effectively mines topic evolution in text data, uncovering dynamic behavioral themes of key users and analyzing evolutionary relationships between topics. Meanwhile, incremental learning is introduced to quantify the dynamic behavioral intentions of key users more precisely. Second, a random wandering strategy based on user interaction degree and propagation depth is designed to address the hidden nature of user relationships. The strategy introduces the user interaction degree designed by the social cognition theory and the propagation depth designed by the propagation chain theory to better explore the hidden user interaction relationships. Finally, for the timeliness of key user identification, considering the advantage of dynamic evolution for real-time interaction, dynamic evolution is introduced to effectively analyze the dynamic structure of topic networks, and attention mechanism is introduced to improve the adaptivity of the model. The experiments show that this paper verifies the factuality of the existence of hidden key users dominating the promotion behind the guiding public opinion, and is more effective in tracing the hidden key users in the topics.

This study is inspired by the current image restoration technology. If we regard the users participating in the rumor as image pixels, similar to social networks, the recovery of pixel data is affected by the pixels themselves and neighbor pixels, then the prediction of user behavior in the rumor diffusion can be regarded as the process of image restoration for pixel-blurred user behavior images. We first propose a diffusion2pixel algorithm that transforms the user relationship network of topic diffusion into image pixel matrix. To cope with the diversity and complexity of the diffusion feature space, the user relationship network is reduced to a low-rank dense vectorization by representation learning before being pixelated by cutting and diffusion. Second, considering the competitive relationship between rumor and anti-rumor, transition matrix of rumor mutual influences is established by evolutionary game theory. A mutual influence model of rumor and anti-rumor is then proposed. Finally, we combine the transition matrix of rumor mutual influence into a simple prediction method Graph-CNN of rumor and anti-rumor topic diffusion based on dynamic iteration mechanism. Experiments confirmed the proposed model can effectively predict the group diffusion trends of rumor, and reflects the competitive relationship between rumor and anti-rumor.