Rumor Propagation Model Research Articles

A Rumor Propagation Model Integrated with Psychological Factors

A Rumor Propagation Model Integrated with Psychological Factors

Dynamics and event-triggered control of rumor propagation model with saturated incidence and time delay on heterogeneous networks

Considering the impact of user’s cognitive ability in rumor propagation, this paper proposes a new susceptible infection recovery (2SIR) rumor propagation model with saturation incidence and time delay on heterogeneous networks. First, the existence of the equilibria is discussed by using mean field theory and basic reproduction number. Second, the dynamic behaviors of the rumor-free and persistent equilibria are analyzed via the local linearization method, Lyapunov stability theory and irreducible matrix properties. Furthermore, an event-triggered impulsive control strategy is proposed to control the rumor spreaders and some corresponding control conditions are given. Finally, some numerical simulation results and a practical example are presented to verify the correctness of the theoretical results and reflect the impact of cognitive ability and time delay on rumor propagation.

Analysis of Rumor Propagation Model Based on Coupling Interaction Between Official Government and Media Websites

The COVID-19 pandemic has not only brought a virus to the public, but also spawned a large number of rumors. The Internet has made it very convenient for media websites to record and spread rumors, while the official government, as the subject of rumor control, can release rumor-refutation information to reduce the harm of rumors. Therefore, this study took into account information-carrying variables, such as media websites and official governments, and expanded the classic ISR rumor propagation model into a five-dimensional, two-level rumor propagation model that interacts between the main body layer and the information layer. Based on the constructed model, the mean field equation was obtained. Through mathematical analysis, the equilibrium point and the basic reproduction number of rumors were calculated. At the same time, stability analysis was conducted using the Routh Hurwitz stability criterion. Finally, a numerical simulation verified that when the basic regeneration number was less than 1, rumors disappeared in the system; when the basic regeneration number was greater than 1, rumors continued to exist in the system and rumors erupted. The executive power of the official government to dispel rumors, that is, the effectiveness of the government, played a decisive role in suppressing the spread of rumors.

Dynamics and Control of a Novel Discrete Internet Rumor Propagation Model in a Multilingual Environment

In the Internet age, the development of intelligent software has broken the limits of multilingual communication. Recognizing that the data collected on rumor propagation are inherently discrete, this study introduces a novel SIR discrete Internet rumor propagation model with the general nonlinear propagation function in a multilingual environment. Then, the propagation threshold R0 is obtained by the next-generation matrix method. Besides, the criteria determining the spread or demise of rumors are obtained by the stability theory of difference equations. Furthermore, combined with optimal control theory, prevention and refutation mechanisms are proposed to curb rumors. Finally, the validity and applicability of the model are demonstrated by numerical simulations and a real bilingual rumor case study.

Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes

Turing patterns for explaining spatial distribution in nature mostly focus on continuous media and existing networks, but only few attempts exist to study them on systems with high-order interactions. Considering that high-order interactions have a particularly significant impact on rumor propagation, this study establishes a generalized reaction-diffusion rumor propagation model based on a multiplex network, where simplicial complexes are employed to describe high-order structures. It aims to provide the spatial distribution patterns of the population participating in rumor propagation and identify the structural factors that affect such patterns. We theoretically provide the necessary conditions for Turing instability in single-layer and multiplex networks by considering high-order interactions. In the numerical simulation, we demonstrate that the Turing pattern could be controlled by adjusting the diffusion coefficient, high-order structure intensity, and average degree of the network. The results indicate that: (i) in a single-layer network, the Turing pattern only exists when high-order interactions appear, and the difference in diffusion rate plays a decisive role, (ii) in a multiplex network, the Turing pattern can still be observed under the same diffusion rates, which are affected by the difference in higher-order intensity between the two layers, and (iii) in existing networks, the average degree of the network has an important impact on Turing pattern. These findings contribute toward comprehending the impact of network structure on pattern formation, particularly the high-order interactions on the Turing pattern.

IRLTS rumor propagation model considering the influence of lurking psychology and the truth of rumors

IRLTS rumor propagation model considering the influence of lurking psychology and the truth of rumors

Dynamic analysis of a rumor propagation model based on a familiarity mechanism and refuters

The spread of rumors not only causes massive economic losses, but also seriously threatens social harmony and stability of human livelihoods. As a result, preventing rumors from spreading is critical. Individuals' familiarity with rumors and refuters has a significant impact on their spread. We develop a rumor propagation model that introduces familiar mechanisms and refuters, as well as demonstrating the existence of an equilibrium point and its local and global asymptotic stability in the model. We also calculate the model's basic regeneration number. Subsequently, we select two appropriate control variables: the contact rate between the ignorant, who are familiar with the rumor, and the disseminators; and the probability that the disseminators will become refuters. We discover that the control effect is optimal when the first variable is minimum and the second variable is maximum. Numerical simulation verifies the accuracy of the obtained results, and sensitivity analysis of the parameters is investigated. According to the research, an increase in the proportion of disseminators who become refuters will slow the spread of rumors, while an increase in the population of ignorant people who are familiar with rumors contacting the disseminators will accelerate the spread of rumors.

A rumor propagation model based on nuclear fission

The propagation of online rumors is rapid, and its propagation mechanism has always been a research difficulty. In this paper, the network rumors are compared to the nuclear fission process, and a network rumors propagation model is constructed. First, the initial online rumors are compared to neutrons, uranium nuclei are compared to individual rumor receivers, and fission barriers are compared to individual active propagation thresholds. Second, the process of nuclear fission is analyzed and the degree of energy accumulation is used to compare the social impact of online rumors. Finally, the online rumor propagation model based on nuclear fission is constructed. Through experimental simulation, it has been shown that compared to classic infectious disease models, this model can better describe the propagation process of rumors in social networks. Through research and analysis, suggestions for suppressing rumors have been proposed, providing new ideas and references for future researchers.

Parameter identification method of information propagation models based on different network structures

In this paper, we create the rumor propagation model with diffusion behavior by considering the state of the rumor in both the time dimension and the space dimension comprehensively. Meanwhile, we demonstrate the reaction–diffusion model using Turing patterns after determining the prerequisites for their occurrence. In order to achieve the purpose of predicting and controlling rumors in time, we choose to utilize the parameter identification technique based on the Barzilai–Borwein (BB) algorithm and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In the numerical simulation section, we first investigate how the rumor avoidance rate and cross-diffusion coefficients affect the propagation of rumors. Then, based on a continuous spatio-temporal system and complex network system, respectively, we perform parameter identification for the propagation model. We thoroughly examine how the type of algorithm, the quantity of unknown parameters, and the network structure affect the identification outcomes in terms of the cost function, error curve, and program function time. When the model constructed in this paper is used for parameter identification on different network structures, the error gap between the final value and the target value is not significant. However, the cost function and time consumption for parameter identification on complex networks are much smaller than on the continuous medium.

A Rumor Propagation Model Considering Media Effect and Suspicion Mechanism under Public Emergencies

In this paper, we collect the basic information data of online rumors and highly topical public opinions. In the research of the propagation model of online public opinion rumors, we use the improved SCIR model to analyze the characteristics of online rumor propagation under the suspicion mechanism at different propagation stages, based on considering the flow of rumor propagation. We analyze the stability of the evolution of rumor propagation by using the time-delay differential equation under the punishment mechanism. In this paper, the evolution of heterogeneous views with different acceptance and exchange thresholds is studied, using the standard Deffuant model and the improved model under the influence of the media, to analyze the evolution process and characteristics of rumor opinions. Based on the above results, it is found that improving the recovery rate is better than reducing the deception rate, and increasing the eviction rate is better than improving the detection rate. When the time lag τ < 110, it indicates that the spread of rumors tends to be asymptotic and stable, and the punishment mechanism can reduce the propagation time and the maximum proportion of deceived people. The proportion of deceived people increases with the decrease in the exchange threshold, and the range of opinion clusters increases with the decline in acceptance.

Dynamic properties of rumor propagation model induced by Lévy noise on social networks

Abstract Social networks are inevitably subject to disruptions from the physical world, such as sudden internet outages that sever local connections and impede information flow. While Gaussian white noise, commonly used to simulate stochastic disruptions, only fluctuates within a narrow range around its mean and fails to capture large-scale variations, Lévy noise can effectively compensate for this limitation. Therefore, a susceptible–infected–removed rumor propagation model with Lévy noise is constructed on homogeneous and heterogeneous networks, respectively. Then, the existence of a global positive solution and the asymptotic path-wise of the solution are derived on heterogeneous networks, and the sufficient conditions of rumor extinction and persistence are investigated. Subsequently, theoretical results are verified through numerical calculations and the sensitivity analysis related to the threshold is conducted on the model parameters. Through simulation experiments on Watts–Strogatz (WS) and Barabási–Albert networks, it is found that the addition of noise can inhibit the spread of rumors, resulting in a stochastic resonance phenomenon, and the optimal noise intensity is obtained on the WS network. The validity of the model is verified on three real datasets by particle swarm optimization algorithm.

Considering individual game behavior and time delay in the multi-channel rumor propagation model

The rapid development of the Internet has accelerated the spread of rumors, posing challenges to social cohesion and stability. To address this, a multi-channel rumor propagation model incorporating individual game behavior and time delay is proposed. It depicts individuals strategically choosing propagation channels in the rumor spread process, capturing real-world intricacies more faithfully. Specifically, the model allowing spreaders to choose between text and video information base channels. Strategy adoption hinges on benefits versus costs, with payoffs dictating strategy and the propagation process determining an individual’s state. By theoretical analysis of the model, the propagation threshold and equilibrium points are obtained. Then the stability of the model is further demonstrated based on Routh–Hurwitz judgment and Descartes’ Rule of Signs. Numerical simulations are conducted to verify the correctness of the theoretical results and the sensitivity of the model to key parameters. The outcomes reveal that increasing the propagation cost of spreaders can significantly curb the spread of rumors. In contrast to the classical ISR model, rumors spread faster and more widely in the improved multi-channel rumor propagation model in this paper, which is a feature more aligned with real-world scenarios. Finally, the validity and predictive ability of the model are verified by using real rumor propagation data sets, indicating that the improved multi-channel rumor propagation model has good practical application and predictive value.

A Deterministic and Stochastic Fractional-Order ILSR Rumor Propagation Model Incorporating Media Reports and a Nonlinear Inhibition Mechanism

A Deterministic and Stochastic Fractional-Order ILSR Rumor Propagation Model Incorporating Media Reports and a Nonlinear Inhibition Mechanism

Dynamic analysis and control measures of rumor propagation model considering true individuals and public opinion individuals

In order to better describe the mechanism of rumor propagation, suppress rumor propagation. This paper examines the influence of true information disseminators on individuals and rumor spreaders who are affected by the public opinion environment. It is found by comparing those who have true information and those who do not have true information in the population. When there are true information disseminators in the crowd, it can effectively control the turn of individuals affected by the public opinion environment to the spread of rumors, and increase the turn of individuals affected by the public opinion environment to the true information disseminators. In addition, this paper analyzes the problems related to the optimal control of the rumor propagation model, and proposes some policies to increase the number of true information disseminators and reduce the number of rumor propagators, so as to better control the spread of rumors. Finally, numerical simulations verify the inhibitory effect of true information disseminators on the spread of rumors. It can be seen from the figure that the earlier the true information disseminator appears, the greater the number of true information disseminators, and the more obvious the inhibitory effect on the spread of rumors.

Dynamic analysis of the rumor propagation model with consideration of the refutation mechanism and activity rate in multiplex networks

With the development of social networks, misinformation including rumors is spreading through multiple channels, which brings many negative effects to society. The government and several Nongovernmental Organizations (NGOs) use relevant rumor rebuttal tactics to control the spread of rumors to prevent these detrimental impacts. In this paper, we consider the responses of different individuals to rumors and classify individuals into three categories: active, normal and inactive, with different activity rates for the three categories. In order to study the interaction between rumor repudiation mechanisms and rumor propagation, we propose a rumor propagation model that considers global and local rumor repudiation mechanisms as well as activity rates in multiplex networks. The upper layer represents the rumor refutation information propagation and the lower layer expresses the rumor propagation. And mean-field equations describing the dynamics of this model in multiplex networks are derived. We found that the threshold of rumor propagation is significantly affected by individual activity rates. Another interesting finding is that the global rumor refutation mechanism suppresses rumor propagation more than the local disinformation mechanism.

A rumor propagation model in multilingual environment with time and state dependent impulsive control

In this paper we are interested in the problem of modeling, analysis and control of rumor spreading on online social networks. As the diversity of languages and the difference of user nodes in the network, a heterogeneous rumor spreading model with two language spreaders is proposed, and the impulsive isolation control strategy is considered. Firstly, the model with periodic impulsive isolation control and time-delay recovery rate is discussed. Based on the stability theory of impulsive differential equations, some conditions regarding the impulsive period and control gains are given for the rumor asymptotically disappearance and persistent existence. Secondly, a new type of event-triggered impulsive isolation control method is developed to address the tradeoff between restraining rumor and minimizing the cost of control, and some conditions are given to make the rumor disappear asymptotically. Furthermore, an improved event-triggered mechanism is established based on the fixed-time stability theory, which provides the conditions for the rumor to disappear within a fixed-time. Finally, some numerical simulations and an actual example are given to verify the rationality of the model and the effectiveness of the control methods.

Turing instability analysis of a rumor propagation model with time delay on non-network and complex networks

With the development of the Internet and social media, rumors can spread not only through word-of-mouth but also rapidly through the network. In this paper, a dynamic model of rumor propagation with time delay is proposed separately for non-network and network scenarios. Additionally, we analyze the equilibrium points and their existence conditions for rumor propagation. After the linear approximation of the model, the necessary conditions for Turing instability are derived. Furthermore, the amplitude equation corresponding to the model in this paper is derived. Finally, through numerical simulations in the non-network model, we validate the aforementioned theories and find that changing the removed coefficient and cross-diffusion coefficient have a significant impact on Turing patterns, while time delay and periodic diffusion have a smaller impact. In the network model, we compare the effects of two network structures, the WS network and the BA network, through numerical simulations, verifying the feasibility of the Monte Carlo simulation method.

The dynamics and control of a multi-lingual rumor propagation model with non-smooth inhibition mechanism.

In this paper, the dynamic behaviors and control strategies of a rumor propagation model are studied in multi-lingual environment. First, an S2E2I2R rumor propagation model is proposed, which incorporates a non-smooth inhibition mechanism. Meanwhile, the existence and stability of the equilibrium are analyzed, grounded in the spreader threshold of the government intervention. Finally, the optimal control and the event-triggered impulsive control strategies are proposed to mitigate the spread of rumors, and the comparison of their effectiveness is further presented by the numerical simulation and a practical case.

考虑加权传播率和随机干扰的谣言传播模型

考虑加权传播率和随机干扰的谣言传播模型

Cross-Domain Social Rumor-Propagation Model Based on Transfer Learning.

Rumors in different topic domains have different text characteristics but similar emotional tendencies. To resolve the scarce-data problem in some rumor-topic domains, this study proposes a cross-domain rumor-propagation model, which is based on transfer learning. First, given the diversity and complexity of the rumor-propagation landscape, this study introduces a novel method, User-Retweet-Rumor2vec (URR2vec), which leverages the power of representation learning to uncover latent features within rumor topics. It also displays the forwarding relationship between users and rumors, user node information, and rumor-topic information in low-dimensional space. To capture the impact of human emotional cognition during rumor spreading, we also introduce a deep-learning model based on the natural language texts of rumor topics, which analyzes the sentiment in the text and uncovers the emotional correlations among users. Furthermore, a rumor-propagation prediction model based on the text-sentiment analysis-graph convolutional network (TSA-GCN) is proposed and pre-trained on existing rumor-topic data to ensure its prediction accuracy. Finally, considering the data sparsity at a rumor-topic outbreak, the trained propagation model is transferred to the rumor topic for prediction. Meanwhile, the rumor topic in different domains has different edges and conditional distribution, similar emotional characteristics, and network structure among the rumor topics. After fine-tuning the parameter and adding a domain adaptation layer in TSA-GCN, a domain adaptation model based on parameter and graph-structure migration is obtained.