Due to comprehensive considerations regarding demand and cost control in traffic management, the performance indicators for checkpoint sensors can differ depending on the section of road. Consequently, some traffic data collected at checkpoints may be incomplete, which complicates data analysis. To address this issue, we propose using a neural network based on embedding synchronized spatio-temporal map data. Firstly, we designed a trajectory vectorization algorithm using word embedding techniques, modeling the road network with vehicle trajectories to capture spatial correlations among road checkpoints. Secondly, we constructed a spatio-temporal synchronization graph recovery neural network (STSGRN) that uses graph convolutional networks (GCNs) and gated recurrent units (GRUs) to account for the spatio-temporal characteristics of traffic data and fill in missing data across multiple dimensions. Finally, we developed an attention mechanism module for the spatio-temporal graph to extract dynamic dependencies at the spatio-temporal level. This architecture enhances the flexibility of the STSGRN in processing complex data with missing values. Experimental results demonstrate that the model effectively identifies spatial and temporal correlations in traffic data, enabling the accurate imputation of missing values. Compared to existing methods, it demonstrates significant improvements and superior generalization performance.

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 significant societal impact of online public opinion has spurred extensive research into the underlying mechanisms of information diffusion. While existing diffusion prediction models have effectively leveraged network structure, they often lack a deep understanding of the intrinsic patterns governing information dissemination, thus limiting their predictive power. To address this critical gap, we introduce PMRCA, a novel information diffusion prediction model inspired by social psychology and driven by fundamental propagation patterns. PMRCA posits that two key patterns underpin topic propagation: multisource resonance, where consensus emerges from consistent narratives across multiple sources, and cognitive adaptation, where information is tailored to align with audience cognitive features. Correspondingly, PMRCA incorporates two dedicated learning tasks: structural contrastive learning based on node homogeneity to capture multisource resonance, and clustering contrastive learning based on cognitive clusters to model cognitive adaptation. Extensive experiments on four real-world datasets demonstrate that PMRCA significantly outperforms existing mainstream models. Importantly, our pattern-driven approach is model-agnostic, allowing it to be flexibly applied to various diffusion prediction scenarios and enhance performance.

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.

In social networks, studying rumor propagation patterns is essential for curbing the spread of rumors. Given the coexistence and conflict of multiple-type rumor information, as well as users’ cognitive differences, this article presents a rumor propagation model grounded in user cognition and evolutionary game theory. First, considering the potential impact of social relationships between users on rumor propagation, the KD-Tree algorithm is employed to uncover hidden connections between users, thereby enriching the topology of the user’s social network. Second, a user behavior driving mechanism for rumor, anti-rumor, and motivation-rumor types is constructed based on evolutionary games to reflect the interactive and strategic nature of users’ responses. Moreover, the Lotka-Volterra equation is utilized to explore the dynamic game of multi-type rumor information and the cognitive process of users. Finally, to address differences in users’ cognition, this article introduces the anti-rumor trust state A and the motivation-rumor trust state M , which arise from users’ exposure to multiple types of rumor information. Based on these trust states, a rumor propagation model, SIAMR, is constructed using user cognition and evolutionary game theory. Experiments demonstrate that the model accurately captures the dynamic interactions between multi-type rumor information and the transmission process of rumor topics in social networks. The proposed model integrates cognitive psychology with a strategic interaction framework, offering a more realistic representation of rumor propagation behavior in the real world. Experimental results reveal that SIAMR improves prediction accuracy by 14.23% over baseline models in simulating the dynamics of multiple types of rumors, effectively capturing users’ cognitive influences and the mechanisms of information competition.

This study was inspired by video forgery detection techniques. If the topic space at a certain time is considered as a frame image, the consecutive frame images over time could be viewed as a video. Then the rumor topic detection problem is transformed into a topic video forgery detection problem. Thus, a novel rumor detection method was proposed. First, a Topic2RGB algorithm was proposed to convert comment users into pixel points. The algorithm views commenting users as pixel points while using game theory to mine user pro-opposition emotions as RGB information. Secondly, a Topic2Video algorithm was proposed to convert the topic space into video. The algorithm converts the topic space into frame images. Meanwhile, the topic space is time-sliced, then the topic space is transformed into a video. Finally, the volatility of user emotional confrontation during a long time in the topic space is like the change of characteristics of frame images in forgeries videos. Then, a topic video rumor detection method (TVRD) was proposed. The experiments indicate that the method successfully verifies the viability of the topic videolization for rumor detection. Additionally, the method also demonstrates the effectiveness of user emotion confrontation of topic space on detection performance.

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.

In social networks, topics often demonstrate a “fission” trend, where new topics arise from existing ones. Effectively predicting collective behavioral patterns during the dissemination of derivative topics is crucial for public opinion management. Addressing the symbiotic, antagonistic nature of “native-derived” topics, a derivative topic propagation model based on representation learning, topic relevance is proposed herein. First, considering the transition in user interest levels, cognitive accumulation at different evolutionary stages of native-derivative topics, a user content representation method, namely DTR2vec, is introduced, based on topic-related feature associations, for learning user content features. Then, evolutionary game theory is introduced by recognizing the symbiotic, antagonistic nature of “native-derived” topics during their propagation. Moreover, implicit relationships between users are explored, user influence is quantified for learning user structural features. Finally, considering the graph convolutional network’s ability to process non-euclidean structured data, the proposed model integrates user content, structural features to predict user forwarding behavior. Experimental results indicate that the proposed model not only effectively predicts the dissemination trends of derivative topics but also more authentically reflects the association, game relationships between native, derivative topics during their dissemination.

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.