Information Diffusion Process Research Articles

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.

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.

Rural community response to accidental toxic gas release: An individual emergency response model during self-organized evacuations

The emergency response ability in rural areas is weaker than in urban areas. Once a major accident occurs in a rural area, it is more likely to cause serious consequences. Thus, we propose a individual emergency response model during self-organized evacuations after accidental toxic gas releases in rural areas. The proposed model can reproduce emergency information dissemination and individual emergency decision-making in self-organized evacuations. We propose an improved susceptible infected recovered model for emergency information dissemination process. At the same time, we propose a emergency decision-making mechanism to simulate individual emergency actions. In this model, individual personality characteristics will affect the accessing and disseminating individual emergency information. Then, we conduct a simulation experiment based on methanol leakage in a rural community. Through comparative experiments, we found that the traditional model may be too pessimistic in estimating emergency information dissemination results, underestimating shadow evacuation. In noncompliance evacuation areas, loudspeakers can inhibit individuals from unnecessarily evacuating who are close to it, and may cause individuals far away from it to evacuate unnecessarily. When formulating emergency response strategies, managers should focus on groups with weak access to information and consider the impact of loudspeaker locations on individual emergency response actions.

Finding robust and influential nodes from networks under cascading failures using a memetic algorithm

In the research of complex networks, how to find a set of nodes in the network with the most extensive range in the propagation process, i.e., the Influence Maximization (IM) problem, is one of the focal topics. Existing studies mainly consider the information dissemination process on networks and how to select diffusive nodes efficiently, but little attention has been paid to changes related to the network structure. In reality, networked systems are exposed to uncertain interferences and even destructive sabotages, and cascading failures are one common destruction that can cause networks to collapse even if only a small number of nodes fail. In the case of various complex environmental factors, how to select robust and influential nodes, i.e., the robust influence maximization (RIM) problem, is of great importance in promoting the realistic application of the influence maximization problem. This paper investigates the RIM problem under cascading failures to address the shortcomings in previous studies. Based on existing research, a new performance evaluation metric, RS-cf, is designed to assess the level of robust influence in a numerical form. For solving the seed determination problem, a Memetic algorithm towards the RIM problem under cascading failures, MA-RIMCF, is designed to find nodes with stable information propagation capability guided by RS-cf. Experiments have been conducted on both synthetic and realistic networks to validate the performance of the algorithm. Results indicate that MA-RIMCF can obtain competitive candidates over existing approaches, and seeds with robust and influential abilities are generated to solve diffusion dilemmas.

Method Toward Network Embedding Within Homogeneous Attributed Network Using Influential Node Diffusion-Aware

Network embedding (NE) focuses on discovering low-dimensional embeddings of nodes while retaining their intrinsic features and structure of nodes. It is essential for many practical applications, containing text mining, community detection, and node classification. However, the great majority of existing systems are incapable of combining structural and attribute information. To tackle the above-mentioned problem, considering the information diffusion process, we present a novel model for attribute NE (ANE), namely influential node diffusion-based matrix factorization (INDMF), which contains topology level and attribute level. In detail, we first propose a novel method to extract high-order information via influential node diffusion sequences. Then, we regard the optimization of our proposed structure-based and attribute-based loss functions as a matrix factorization problem. Furthermore, this model can be used to generate final node embedding by aggregating the topology level and attribute level hierarchically. Experiments are conducted on four real-world datasets, which indicates that INDMF beats all competing algorithms in node categorization, community detection, and graph visualization.

Information shock, market reaction, and stock message board information diffusion

We investigate the market reaction to information shocks in the Chinese stock market and study the process of information diffusion among investors on the stock message board. We find that investors tend to overreact to information shocks and the communications on the stock message board facilitate this overreaction. Further analysis shows that stock market overreaction is more significant in response to small and medium-cap shocks, and the degree of overreaction decreass with increasing liquidity. Our empirical results provide significant evidence of how information spreads among retail investors from the perspective of investor overconfidence and information asymmetry.

Implications of social network structures on socially influenced decision-making

Individuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.

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.

I-SFI model of propagation dynamic based on user’s interest intensity and considering birth and death rate

ABSTRACT Everyone has a different level of interest in a trending topic posted on social media, which may affect user’s behaviour. In order to find out the way it affects the process of information transmission, we construct an interest intensity-based susceptible-forwarding-immune I − SFI propagation dynamic model and two parameters birth rate A and death rate μ are introduced to represent the users who newly join the group of disseminated information and the users who leave this population. And we give different birth rates to people with various levels of interest, which helps us to determine the interest intensity of potential users to a certain extent. We use the forwarding data of the real topic on Chinese Sina-microblog for data fitting, which can accurately parameterize the model and quantify the impact of interest intensity. And sensitivity analyses also give some strategies for increasing the impact of information dissemination process from the perspective of interest intensity.

Public Relations Management After COVID-19 Lockdown: A Case Study of Wuhan, China

The aim of the research study was to understand the effectiveness of the public relations strategies of crisis communication and how impactful they were for saving the image of Wuhan city after the pandemic. Wuhan was hailed as the epicentre of Corona virus infections as the initial infections have spread from this place. Thus, the image of this prominent city was maligned not only towards the local and national audience, but the international audience as well. Thus, the Wuhan government took up most of the necessary public relations strategies to restore the image of the city among the national and international audience. This paper has intrinsically dealt with the opinions from the different sample population on the effectiveness of the crisis communication done by the PR strategists of the Wuhan government. From most of the respondents it has been found out that Wuhan government and its public relations strategies were wholly operated through the digital medium and this has helped in restoring the image of the image to greater and effective extent. Through the disposition of information through different social media networking sites like Facebook and Sina Weibo, the PR strategists were able to provide information on the improvements in the city condition and the reduction of infection in Wuhan along with other positive news articles. However from the respondents it has been also found that, including lesser opinion from the public, decrease in the participation of the common mass in the information dissemination process by the Wuhan government has decreased the effectiveness of the image restoration PR strategy in the Wuhan city. The research study concludes that while the PR crisis communication and its strategies have been helpful towards restoring the image of the city, there are still some loop holes that needs to be fixed.

Extracting the diffusion dynamics of crisis information on online social networks: Model and application

Disaster mitigation greatly benefits from the diffusion of crisis information. To learn about the diffusion dynamics of crisis information on online social networks, this paper puts forward a crisis information diffusion model based on population dynamics and further analyses its parameters, such as ordinary users’ average number of followers, the number of potential audience members who might spread the crisis information, the forwarding probability for each audience member and the attenuation speed of the information based on real data from Sina Weibo. In addition, a method for the prediction of crisis information diffusion is proposed and verified on the basis of this model. The results show that this model could describe and predict well the diffusion process of crisis information on online social networks, enabling relevant departments to develop appropriate strategies for crisis information diffusion.

Social media opinion leaders and information diffusion of crowdfunding projects: Evidence from China

Opinion leaders play essential roles in online social media and are the key to analyzing the process of information diffusion. While the crowdfunding market has exhibited rapid growth, empirical studies about social media opinion leaders in the crowdfunding context remain nascent. Specifically, efforts to identify opinion leaders in social media and to understand the mechanisms by which such opinion leaders influence others in the crowdfunding context are limited. Based on the information diffusion theory, this study aims to address these research gaps by applying a multiple-case research design combined with dynamic network analysis (DNA). By analyzing user-generated content related to selected case projects from Weibo, the most popular micro-blogging site in China, the study identifies five types of social media opinion leaders. In addition, it investigates the various roles of opinion leaders in the information diffusion process on social media. Theoretically, this study broadens the boundaries of the “two-step flow” model and the diffusion of innovations theory by applying them to the crowdfunding context. The findings of this study provide valuable insights on crowdfunding project promotion for both crowdfunding platform operators and project initiators.

Research on the Construction of University Cultural Brand IP Driven by Network Cloud Platform Technology

Abstract Campus culture brand IP, as the soft power in the construction of university brand culture, has become an important material carrier for the inheritance and dissemination of university brand culture. In the cloud platform technology, we are using an information diffusion model to explore the diffusion degree and influence of college culture brand IP information on the network platform. The dissemination process of cultural brand IP information is examined from the point of view of sharing information resources in colleges and universities. Construct a competitive information dissemination system based on the user nodes of online social networks and the interrelationship between users. Promote the dissemination and diffusion of university cultural brand IP information on online social networks using a data-driven approach. The results show that: k value is 0.12, the promotion effect of college culture brand IP is 2.59621e+04, and the relative risk index is 0.354264. Within 0-6 hours, the maximum number of IP views reaches 34,000 times, which indicates that the authority of the college platform and the information diffusion model can improve the diffusion of college culture brand IP.

Assessment of the main features of the model of dissemination of information in social networks

<span lang="EN-US">Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.</span>

Exploring the Research on Media Communication Strategies of Public Welfare and Charity Organizations in the Context of Social Governance Innovation

Abstract In the context of social governance innovation, the integration and sharing of information are studied through the construction of an information-sharing platform. The process of interpreting the evolution of new media information dissemination includes analyzing the characteristics and laws of information dissemination on various platforms. Based on the existing scale of public welfare charities, the kinetic evolution equation of the public welfare information dissemination model is established to describe the dissemination process of public welfare information and its influencing factors. The impact of media intervention on the final scale of public welfare information dissemination is analyzed using the collected Facebook dataset as the background, and it is found that the higher the media credibility is at t = 4, the public welfare information attracts media attention. After the release of public interest information, the immunity node began to show a gradual upward trend, reaching a maximum of 1.0. Indicating that after the media release of public interest information, the final scale of dissemination of public interest information increases.

Study on the influence of the information diffusion and vaccination behavior on disease transmission in multiplex networks

To investigate the influence of information diffusion and vaccination behavior on disease transmission. In this paper, a coupling model is proposed to describe the co-evolution process of information diffusion, vaccination behavior and disease transmission in multiplex network. In the information layer, two types of information are diffused, including the positive information and the negative information, respectively. During the process of information diffusion, the influence of mass media on information diffusion is considered, due to the herd effect, which type of information is believed to depend on the information state of the surrounding neighbors based on the Heaviside step function. In the behavioral layer, the individuals with different types of information will choose different immune behavioral responses, which in turn will influence the disease transmission. The coupling model was analyzed by using the Micro Markov Chain Approach (MMCA) to obtain state transition equations and the prevalence thresholds for disease. By simulating the simulation experiments, it demonstrates that the information diffusion has no direct effect on disease prevalence thresholds, but affects the proportion of infected; vaccination behavior has a significant effect on disease prevalence thresholds and also affects the proportion of infected. The results of the study suggest that when the disease transmission is within the controllable range, the stronger the diffusion of positive information, the higher the vaccination rate, which has a more positive influence on inhibiting disease transmission; once out of control, the official will lose trust, which is not conducive to disease control.

Who politicized the COVID-19 pandemic on Twitter: cultural identity and Chinese prejudice in a virtual community

This research, using the cultural-identity-protective cognition theory and focusing on China as a case study, aims to identify influencers, track information dissemination processes, and recognize the patterns of information and emotional flows within Twitter conversations related to the politicization of the COVID-19 pandemic. 5.3 million Twitter posts were collected related to the coronavirus using keyword searches such as #Wuhan and #Chinesevirus. Out of these, 17,964 tweets were selected as a sample from the most commented tweets by top users. A qualitative textual analysis revealed that Twitter users’ attitudes toward the coronavirus disease and governments’ policies were highly politicized and polarized. Both influencers and general users had the capacity to steer Twitter conversations into the political realm, thus contributing to the politicization and polarization of discussions during the information flow. Notably, opinion leaders, especially among influencers, could politicize and polarize a conversation by initially indicating their political standing in the tweet. In general, Twitter users had the ability to make neutral and objective news information go viral if a large number of Twitter users continue the politicization and polarization processes. Group membership—specifically, political standing—and cultural identities played significant roles in perpetuating a vicious cycle of anger. This cycle amplified identity threats for Twitter users and prompted more prejudicial and polarized comments, especially when China, Chinese people, and the Chinese government were the subjects of blame in the Twitter community.

Analysis of the information transmission path of national sports culture based on gradient descent algorithm in the era of informationization

Abstract The digitization inheritance of national sports culture is an inevitable requirement of the new era, so studying its communication characteristics in the digital age is necessary. In this paper, the cognitive semantics of its cultural imagery is obtained based on the analysis of the connotation of national sports culture, and the informational imagery is extracted based on the cultural semantic analysis. The fast gradient descent algorithm optimizes the sampling and dissemination process of information during establishing the SIRI communication network for cultural imagery. Changes in the state and popularity of the dissemination nodes were explored by changing the external social environment and the input of the information carrier. When the probability of social environment increases from 0.15 to 0.95, the node with state S transforms into an information dissemination node faster and finally tends to a stable value of 0. In terms of information carriers, the degree of the flow of video dissemination will be graded higher than other carriers, and the popularity of video dissemination is 2.45%, 3.14%, and 5.21% higher than audio, image, and text, respectively, when the dissemination reaches the 4th generation. The information dissemination model optimized using a gradient descent algorithm can accurately capture the dissemination characteristics of national sports culture, thus proposing a better information transmission path.

The development of digital model of college Civics teaching combined with information diffusion model

Abstract In this paper, the information sphere radiation model describes the diffusion process of information in cyberspace. In real space, the information ring radiation model represents how information diffuses through interpersonal transmission in social circles. Secondly, the degree distribution of nodes in the network is described by the spatial topology’s degree distribution function and how information diffusion in the network is constructed. Finally, the model’s effectiveness is verified through the simulation of the total number of nodes function Q(t) for the diffusion of information in Civic and Political Science teaching. The mean diffusion degree is around 1.2 according to the results when teachers and students are the main interactors. In the case of student-content interaction and hybrid interaction, the mean value of diffusion is around 2.3. In the exercise session, however, the student-student diffusion degree reached 3. Through the information diffusion model, this paper facilitates the development of a digital model for college Civics teaching.

Cultivation of students’ craftsmanship identity and ability based on the theory of directed percolation

Abstract In this paper, data mining is performed based on the directed percolation theory influence mining algorithm, analyzing the maximum influence node mining proportion threshold and calculating the maximum eigenvalue of the non-return matrix. The convergence conditions of the iterative process of immobile points are analyzed to obtain the percolation threshold of the information dissemination process of directed percolation, and the initial vector is selected among the node-associated features to form the iterative formula to mine the maximum percolation theoretical influence. According to the results, students' focus on craftsmanship has risen significantly, and the percentage of students who consider persistent concentration spirit important has increased to 75%. The directed percolation theory's craftsmanship spirit is crucial for developing students' vocational skills and comprehensive literacy.