Information Diffusion Model Research Articles

Relieving Overexposure in Information Diffusion Through a Budget Multi-stage Allocation

When information dissemination campaigns on Online Social Networking platforms are too aggressive, this can easily cause information overexposure. Overexposure can break through the psychological and physiological limits that the audience can tolerate, making it difficult for the audience to obtain reasonable cognitive concepts. For example, the overexposure of information referring to an object in a promotional campaign can lead to inflated expectations in individuals. Building on this, we introduce two indicators for individuals’ expectations and actual utility of the object and design a multi-stage triggered mechanism for seed individuals to explore the relieving overexposure problem in information diffusion. We build a multi-stage information diffusion model and characterize the evolution of individual expectations. We verify the hardness result of the relieving overexposure problem by budget multi-stage allocation, and the non-submodularity and non-monotonicity of the objective function. Addressing the non-monotonic and non-submodular set function, we provide a direct influence-oriented algorithm with a greedy approach. Extensive experiments are performed on four real networks to explore how model parameters and network properties affect the effects of multi-stage triggered strategies for seed individuals. Using the experiments, we found that the seed individual multi-stage incremental triggered strategy of dissemination campaign of information referring to an object shows better performance, and the lower the actual utility of the specific object, the more accurate the promotion strategy needs to be developed.

IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers

Abstract Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA (Information Diffusion model for integrating gene Expression and EEG data in identifying Alzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and EEG data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that
IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10-13 Hz) frequency band yielded 8 AD associated genes out of the top 100 most frequently selected genes by our algorithm, with a p-value of
0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings.

Communication dynamics of congestion warning information considering the attitudes of travelers

Traffic congestion is a serious problem faced by many cities worldwide today. Congestion warning information is one of the important influencing factors of urban road congestion; To this end, based on the dynamics of infectious diseases, a congestion warning information dissemination model considering the attitudes of travelers and the network structure was constructed. The existence and stability of the equilibrium points of non congestion warning information and congestion warning information in the model were analyzed, and the optimal control strategy of the model was proposed. Numerical simulation was conducted to verify the results of theoretical analysis, simulate and analyze the impact of changes in various parameters in the model on the dissemination of congestion warning information, and perform sensitivity analysis on several parameters. The results indicate that travelers are more inclined towards “fast” modes of transportation and have a stronger willingness to share congestion warning information. The dissemination range of warning information is wider, which can play a positive role in reducing traffic congestion pressure.

Is disinformation more likely to spread? A fuzzy-set qualitative comparative analysis of emerging infectious diseases on China’s short video platform

ObjectivesThis study aimed to develop a model for the dissemination of information on emerging infectious diseases (EIDs) by identifying the specific features of mpox (monkeypox)-related short video content that encourages...

Hypergraph-Based Influence Maximization in Online Social Networks

Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms.

An agent-based model of cross-platform information diffusion and moderation

Social media platforms are highly interconnected because many users maintain a presence across multiple platforms. Consequently, efforts to limit the spread of misinformation taken by individual platforms can have complex consequences on misinformation diffusion across the social media ecosystem. This is further complicated by the diverse social structures, platform standards, and moderation mechanisms provided on each platform. We study this issue by extending our previous model of Reddit interactions and community-specific moderation measures. By adding a followership-based model of Twitter interactions and facilitating cross-platform user participation, we simulate information diffusion across heterogeneous social media platforms. While incorporating platform-specific moderation mechanisms, we simulate interactions at the user level and specify user-specific attributes. This allows practitioners to conduct experiments with various types of actors and different combinations of moderation. We show how the model can simulate the impacts of such features on discussions facilitated by Reddit and Twitter and the cross-platform spread of misinformation. To validate this model, we use a combination of empirical datasets from three U.S. political events and prior findings from user surveys and studies.

Expanding public health initiatives through community pharmacies and student pharmacists: A programmatic case study

BackgroundCommunity pharmacies are critical to the public health infrastructure in the United States and provide reliable information for public health concerns. Public health agencies curate educational materials that community pharmacy teams can disseminate. Student pharmacists participate in experiential learning at community pharmacies which could be utilized for dissemination of these resources. ObjectivesThe objectives of this project were to (1) design a model for dissemination of public health information at community pharmacies; and (2) evaluate both the dissemination model’s reach within communities and student pharmacist learnings from engagement in the model. MethodsWe engaged student pharmacists in a model to disseminate information at community pharmacies for 2 Centers for Disease Control and Prevention initiatives about Opioid Use Disorder Anti-Stigma and Antibiotic Stewardship Education. The number of pharmacies and student pharmacists who participated from 2021 from 2023 was retrospectively reviewed to demonstrate programmatic reach. A retrospective text mining of student assignments was conducted to evaluate student experiences. Descriptive statistics were used to report quantitative data. An inductive, rapid content analysis was completed for qualitative data. ResultsAcross 3 years, 333 student pharmacists participated. Students reached 121 community pharmacies, 139 practicing pharmacist preceptors, and over 2000 patients with education and resources. Eleven student learning points emerged from the qualitative analysis. These included learnings around opioid use disorder and antibiotic stewardship. Students also acknowledged that there are public health needs present in communities and that community pharmacy teams are well-positioned to address these needs. ConclusionEngaging student pharmacists to distribute curated information from public health authorities, both to pharmacist preceptors and patients at community pharmacies, is one way to educate future pharmacists, pharmacy teams, and communities on public health priorities. Pharmacies can serve as key venues in communities for dissemination of reliable public health information.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

Measuring the centrality of nodes in networks based on the interstellar model

Measuring the centrality of nodes in a network is a vital and significant task in analyzing the influence of nodes and information dissemination. Existing methods measure the centrality of nodes mainly from local, global and semi-global network perspectives. Currently, semi-global centrality metrics are valued due to the fact that they can achieve high accuracy close to that of global centrality metrics with a slight increase in time complexity over local centrality metrics. In this paper, a novel semi-global centrality metric based on the interstellar model is proposed. First, an interstellar model of information dissemination is constructed and a variable velocity is set for the information after it leaves the source node, while velocity at which the information arrives at the next node determines whether the information can be further forwarded. Then, the attraction slingshot effect is considered to express the facilitation provided by the nodes that forward the information. The centrality of a node can be measured by the number of all nodes, including itself, that can forward the information sent by that node. Experiments with some popular centrality metrics are conducted on some real-world networks, and the results show that the proposed centrality metric has better performance without significant increase in time complexity, and also provides a plausible explanation for information forwarding.

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

IMNE: Maximizing influence through deep learning-based node embedding in social network

Influence Maximization (IM) is a critical problem in social network analysis and marketing. It involves identifying a subset of nodes in a social network whose activation or influence can lead to the maximal spread of information, ideas, or behaviors within the network. Although many approaches have been developed in the literature to deal with this problem, most of these approaches are ineffective in dealing with large-scale social networks due to free parameters and computational complexity. Embeddings are used to learn low-dimensional representations of nodes in a social network. These embeddings capture the structural and semantic information of nodes and their relationships within the network. By training deep learning models on graph-structured data, node embeddings can capture complex patterns and dependencies in social networks, enabling more effective downstream tasks such as IM. Accordingly, this paper proposes an efficient algorithm to address the IM problem in social networks using deep learning-based Node Embedding (IMNE), which includes shell decomposition, graph/node embedding, and search space reduction as well as the use of local structural features. Our approach combines the power of deep learning for representation learning with the rich structural information present in social networks to address the challenge of IM in complex and dynamic social networks. IMNE uses the Independent Cascade (IC) information diffusion model to determine the labels needed to train the model by calculating the influence of nodes. Experimental results on several real-world networks considering different performance metrics show that IMNE performs better compared to existing baseline and state-of-the-art methods.

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization (IM) has drawn significant attention in recent years. Most existing IM methods primarily focus on homogeneous networks, and do not take into account the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real world, encompassing rich semantics and complex structural information. Additionally, the clustering characteristics inherent in a network have a critical and substantial impact on the process of information diffusion, which is often overlooked in IM models designed for heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, which we have named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and incorporates a clustered cascade (CC) model as an information diffusion model to enhance the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models using three academic heterogeneous networks extracted from the DBLP dataset. The experimental results on influence spread demonstrate that SCHGT-IM is superior to fourteen state-of-the-art algorithms and is highly effective in selecting influential seed nodes of different types from heterogeneous networks.

Model of Information Dissemination in the Context of Reputation Formation of an Auditing Company: Official Sources or “Word of Mouth”?

The reputation of auditing firms is of paramount importance in ensuring trust and confidence in financial markets. This article investigates the information dissemination model and its impact on the reputation formation of auditing companies, focusing on the choice between official sources (formal channels, corporate communications, regulatory disclosures, etc.) and word-of-mouth mechanisms (informal channels, client recommendations, corporate gossip, etc.). The research reveals the complex dynamics between formal and informal information dissemination strategies (official sources provide trust and transparency, while word-of-mouth mechanisms offer detailed information and trust) and their implications for reputation management in the auditing industry. The relevance of this research problem lies in the critical role of auditing firms in supporting transparency and honesty in financial reporting, especially after corporate scandals and regulatory scrutiny. The primary aim of this research is to understand the relative effectiveness of different information dissemination models in shaping the reputation of auditing companies. The choice of research subject is justified by the significant influence of auditing firms on financial markets, corporate governance, and investor trust. Using VOSviewer 1.6.16 software, the article conducts a bibliometric analysis of English-language articles and conference abstracts indexed in the Scopus database from 2007 to 2023 (1177 publications) using the keywords “Reputation” and “Auditing Firm.” The analysis confirms the increasing scholarly interest in this topic and identifies 8 thematic clusters, the largest of which combines corporate, image, and social reputation with stability and consistency. The empirical part of the research involves constructing a polygamous model similar to the SIR model, which describes the behavior of three groups of subjects depending on the presence of information and actions regarding its dissemination (active, passive, neutral). The article models the intensity of changes in the number of group members considering various phenomenological parameters (e.g., intensity of communicative processes between groups, structure of social system connections, mathematical expectation of time required for a subject to transition from one group to another, etc.). The results of this research have practical implications for auditing firms, regulatory bodies, and stakeholders in the financial sector. By understanding the relative strengths and weaknesses of different information dissemination models, auditing firms can adapt their communication strategies to effectively enhance their reputation. This research contributes to the ongoing discourse on reputation management and trust-building in financial markets.

The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Network Models

Information diffusion models on networks are at the forefront of AI research. The dynamics of such models typically follow stochastic models from epidemiology, used to model not only infections but various phenomena, including the behavior of computer viruses and viral marketing campaigns. A core question in this setting is how to efficiently detect the most influential vertices in the host graph such that the infection survives the longest. In processes that incorporate re-infection of the vertices, such as the SIS process, theoretical studies identify parameter thresholds where the survival time of the process rapidly transitions from logarithmic to super-polynomial. These results contradict the intuition that the starting configuration is relevant, since the process will always either die out fast or survive almost indefinitely. A shortcoming of these results is that models incorporating short-term immunity (or creative advertisement fatigue) have not been subjected to such a theoretical analysis so far. We reduce this gap in the literature by studying the SIRS process, a more realistic model, which besides re-infection additionally incorporates short-term immunity. On complex network models, we identify parameter regimes for which the process survives exponentially long, and we get a tight threshold for random graphs. Underlying these results is our main technical contribution, showing a threshold behavior for the survival time of the SIRS process on graphs with large expander subgraphs, such as social network models.

Potential Ecological Risk Assessment of Soil Heavy Metals in Fengdong New District Based on Information Diffution Model

The large-scale construction of new districts has led to severe soil heavy metal pollution. Therefore, taking Fengdong New District as the target research area, the descriptive statistics of heavy metal content characteristics and Kriging interpolation analysis have been conducted, and the potential ecological risk index and information diffusion theory were further combined to create an information diffusion model based on risk assessment. Finally, the pollution degree, ecological risk, and risk occurrence probability of Pb, Cu, Cd, and Hg were discussed. The findings revealed that the average concentrations of the four heavy metals far exceeded the background value of soil heavy metals by a factor of 1.943 (Pb), 1.419 (Cu), 3.074 (Cd), and 3.567 (Hg), respectively. Moreover, the distribution of soil heavy metals showed strong variability(CV>65%)owing to human interference. The distribution of Pb and Cu pollution were predominantly influenced by industrial production and land development for construction purposes, whereas industrial activities, agricultural practices, and transportation served as the primary sources of Cd contamination. On the other hand, industrial construction emerged as the major factor contributing to Hg pollution. The average values of individual potential ecological risk index for heavy metals of 9.716 (Pb), 7.095 (Cu), 92.292 (Cd), and 142.469 (Hg), coupled with the regional comprehensive potential ecological risk index (RI) average of 251.573, signified that the region was overall characterized by a relatively high potential ecological risk status. The overall potential ecological risk for Pb and Cu in the region were mild, whereas Cd and Hg posed moderate to high risks, indicating that Cd and Hg were the dominant driving factors behind regional heavy metal pollution. The evaluation results of the information diffusion model based on the potential ecological risk indicated that the probability ranking of different levels of comprehensive potential ecological risk was as follows:slightly high (38.98%) > moderate (38.55%) > high (5.89%) > slight (5.15%) > extremely high (3.56%). The exceeding probabilities of potential ecological risk levels for Cd and Hg were significantly higher than those for Pb and Cu. The exceeding probability of different pollution levels of Hg were slight (94.89%), moderate (66.85%), slightly high (23.62%), high (3.9%), and extremely high (2%), of which only the surpassing probability of the slight level was lower than that of Cd. The prediction error of pollution probability of each potential ecological risk level was less than 5%, demonstrating the reliability of the information diffusion model based on the risk assessment. This research will provide technical reference and support for the monitoring and management of potential ecological risks from soil heavy metals in limited sample data regions.

Dynamics analysis and optimal control study of uncertain information dissemination model triggered after major emergencies

In order to effectively prevent and combat online public opinion crises triggered by major emergencies, this paper explores the dissemination mechanism of uncertain information on online social platforms. According to the decision-making behavior of netizens after receiving uncertain information, they are divided into eight categories. Considering that there will be a portion of netizens who clarify uncertain information after receiving it, this paper proposes a SEFTFbTbMR model of uncertain information clarification behavior. The propagation dynamics equations of the model are given based on the theory of differential equations, the basic regeneration number R0 of the model is calculated, and the existence and stability of the equilibrium point of the model are analyzed. The theoretical analysis of the model is validated using numerical simulation software, and sensitivity analysis is performed on the parameters related to R0. In order to reduce the influence caused by uncertain information, the optimal control strategy of the model is proposed using the Hamiltonian function. It is found that the dissemination of uncertain information among netizens can be suppressed by strengthening the regulation of social platforms, improving netizens’ awareness of identifying the authenticity of information, and encouraging netizens to participate in the clarification of uncertain information. The results of this work can provide a theoretical basis for future research on the uncertain information dissemination mechanism triggered by major emergencies. In addition, the results can also provide methodological support for the relevant government departments to reduce the adverse effects caused by uncertain information in the future.

Influencers and Influence: the dissemination of information in times of fake news and post-truth

The end of 2023 was marked, on Brazilian social networks, by a tragedy: the death of a 22-year-old young woman, influenced by a relatively new phenomenon in the social spectrum, the so-called fake news. Given the repercussion of the fact, this work has as its object of study the phenomenon of information dissemination on social networks, under the scope of social network X (formerly Twitter). Based on an information diffusion model, this research has, as a general objective, to evaluate the dissemination of information on social network. Specific objectives include investigating the relevance of the information dissemination model to the issue of disseminating fake news; test the information diffusion model with experimental data obtained from the social network X and validate the results obtained in numerical simulations arising from the information diffusion model. It is concluded, therefore, that the lower the estimated real reach of a post, the longer the information dissemination process will take, which would justify the introduction of joint work between different profiles in partnership with bots to increase the engagement of a post.

Mathematical and Computer Modeling of a Dynamic System for Effectively Combating Disinformation

The work investigated a mathematical and computer model of a dynamic system for effectively combating disinformation. In the compartmental model of false information dissemination in society, there are groups of citizens: - At risk, prone to the perception of misinformation; Adepts - those who accepted false information and with Immunity - those who rejected false information from the very beginning or future adepts. A barrier level for the number of adherents will be introduced as a measure of the information security of society. As a result of a computer experiment, the possibility of an uncontrolled growth in the number of adherents was identified, threatening the safety of society as a whole.

Appreciation and Dissemination of Chinese Paintings from a Cross-cultural Perspective Based on Information Diffusion Modeling

Abstract With a long history, profound cultural deposits, and diversified art types, Chinese painting is both an important part of traditional Chinese culture and a typical representative of traditional oriental art and culture. This article starts with the spirit of Chinese painting and aesthetic appreciation and then explores the significance of Chinese painting’s foreign dissemination and dissemination path. For the appreciation of Chinese paintings, the article designs an index system for assessing the appreciation ability of Chinese paintings, solves the weights of the indexes by using the optimal assignment method of distance estimation, and constructs a model for assessing the appreciation ability of Chinese paintings by combining with the cloud model. The Hidden Markov Model is used to establish an information diffusion model for Chinese painting dissemination, and the parameters of the model are estimated using the Great Likelihood Method. The quantitative analyses of Chinese painting appreciation ability, information diffusion effect, and satisfaction with communication effect were carried out with students from University S as the research subjects. The main factor of Chinese painting appreciation ability is emotional ability, which has a weight share of 26.74%. The comprehensive assessment cloud eigenvalue is (3.4714, 0.1462, 0.0348), which shows that the appreciation ability is good. When the state transfer probability is raised from 0.3 to 0.9, the increase in the number of Chinese painting communication nodes in 25s is 93.59%, and the satisfaction index of the audience for the communication effect of Chinese painting is 3.899. Combining with the information diffusion model, we can understand the change of communication nodes in Chinese paintings, which can provide a guide to enhance the communication effect of Chinese paintings.

Information flow and data-driven analysis of cross-cultural communication in the Chinese cultural community

Abstract The rapid development of the Internet also provides the possibility of information flow for cross-cultural communication, and the network not only breaks through the boundaries of time and space but also breaks through the limitations of cultural communication barriers. The study analyzes the influence mechanism of cross-cultural communication through the medium of social networks. A series of characteristics are proposed to characterize the flow of information, and a simulation experiment is carried out on the influencing factors of cross-cultural communication using the infectious disease model (SEIR). We utilize the Twitter dataset of real social networks to examine the effectiveness of the proposed SEIR model of information dissemination and verify its accuracy. The simulation experiment analyzes the influence of the containment mechanism, the time lag mechanism, and the forgetting mechanism on the model of information dissemination in cross-cultural communication, and information dissemination does not occur when R 0 <1. When R 0 >1, the cross-cultural communication information will be continuously and stably spread in the social network. This paper’s SEIR model has a positive effect on the dissemination of cross-cultural communication information, as evidenced by the results.