In contemporary society, online public opinion exerts significant influence, and guiding its trajectory is essential for maintaining social stability. To capture the interaction and evolution of competing guiding information during topic dissemination, a competitive guidance topic propagation model based on evolutionary game theory is developed. First, to address the interactive influence of multiple topic features, a multifactor analysis framework is established. Based on positive and negative sentiment, a metric for topic controversy is designed. Considering the effectiveness of multiple regression in measuring diverse features, multiple regression is introduced to evaluate the driving forces of different topic characteristics. This approach allows a detailed analysis of how various features affect both the dissemination impact and the dissemination potential. Second, to address the competition and cooperation among different types of guidance information, evolutionary game theory is introduced to design a user participation-driven game mechanism. The mechanism is used to analyze the competitive and cooperative dynamics between diverse guidance messages. It reveals the strategic landscape of positive and negative information dissemination and provides a theoretical foundation for modeling user state transitions. Finally, to address the dynamic nature of user state changes and considering the similarity between infectious disease transmission and topic propagation, a suspicion–hesitation–positive and negative information–immunity model is proposed. The model allows fine-grained modeling of user state transitions and provides effective theoretical support for public opinion management. Experimental results demonstrate that the proposed model effectively fits the propagation trends of guiding topics on the given dataset and captures competitive and cooperative relationships among different types of guiding information. Compared with baseline models, performance improves by at least 2.8%, providing a solution for modeling and analyzing the propagation process of guiding topics.

In the topic dissemination space, various types of messages interact and collectively influence the propagation trends of derivative topics. To address this, a prediction model for group behavior in hotspot topics is proposed based on multitype complex messages. First, considering the complexity and high-dimensional nature of data in the topic feature space, sparse representation is employed to extract key user attributes in the derivative topic space and to calculate sparse vectors. Based on these sparse vectors, a node feature prediction submodel is subsequently constructed using regression algorithms. Second, considering the cooperative and competitive relationships between different message types, evolutionary game theory is applied to transform the dynamic interactions between messages into psychological games among users. By reflecting the optimal benefits of user strategies, it captures the driving forces of derivative messages and establishes a dynamic topic dissemination network. Additionally, a graph convolutional network is employed to extract user structural features, constructing a structure attribute prediction submodel. Finally, to overcome the limitations of individual submodels, the node feature and structure attribute prediction submodels are integrated to form a group behavior prediction model based on multitype message interactions. The input data is discretized using time slicing to enhance the model’s generalization ability. Experiments show that the proposed model accurately captures the complex interactions among multitype messages in the derivative topic space and effectively predicts group behavior and hotspot topic propagation trends.

As topics evolve during the dissemination process, their derivative characteristics play an important role in revealing the mechanism of topic dissemination in social networks. Due to users’ cognitive inertia, followers of antecedent topics tend to pay more attention to derivative topics than ordinary users, and this cognitive inertia is directly related to the correlation between these two topics. Based on this finding, we propose a derivation topic dissemination model based on iterative multitopic derivation and social psychology, taking into full consideration users’ emotional accumulation of antecedent topics and repeated derivation of topics. First, a multiple linear regression model is used to construct a metric algorithm for user antecedent sentiment and to effectively analyze the dynamics of antecedent sentiment accumulation affecting the spread of derivative topics. Second, to analyze the interactions between and within multitopic layers in full, an iterative multitopic dissemination model iterative-susceptible infectious recovery (SIR) is proposed. In addition, the association degree is introduced to define the cross-model state transition equation by considering the association and differences among multitopics. Last, considering the influence of cognitive inertia and “continuous attention psychology” in social psychology, we construct a user psychology-based driving force model which can further improve the cross-model state transition equation. According to the experiments, the model can effectively reveal the influence of different factors on the dissemination trend of derivative topics in social networks, as well as depict the dissemination dynamics of derivative topics.

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.

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.

This article is inspired by image forgery recognition techniques. If we regard topic comments as image pixels, the whole topic is a complete image. The image differences between rumor topics and nonrumor topics are reflected in image pixels just like forged images, and then, the problem of detecting rumor topics can be regarded as the problem of recognition images of rumor topics. First, the Topic2Image algorithm is proposed to use the semantic information to quantify the adversarial intensity among comments. It is mapped to the topological relationship among user comments. Also, the relative positions of the comment nodes are determined by the adversarial intensity. Second, considering the competitive relationship between positive and negative comments, a sentimental mutual influence model is proposed. Based on the evolutionary game theory, a transfer matrix of sentimental mutual influence is constructed. Internal and external factors of rumor detection are considered at the individual and group levels, respectively. Finally, considering the advantages of convolutional neural network (CNN) for image processing, a simple rumor detection algorithm topic image rumor detection (TIRD) based on topic image classification is proposed. Using CNNs and gray-level co-occurrence matrix to extract global and local features of topic images and combining them with the transfer matrix of sentimental mutual influence, the detection of topic rumor is realized. Experiments demonstrate the feasibility of transforming topic rumors into image. In addition, the effectiveness of image forgery recognition technology for detecting rumors is verified.