Dynamic properties of rumor propagation model induced by L´evy noise on social networks

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,L´evy noise can effectively compensate for this limitation.Therefore,a susceptible–infected–removed rumor propagation model with L´evy 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 Barab´asi–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.

CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

An improved ISR-WV rumor propagation model based on multichannels with time delay and pulse vaccination

The rapid development of the Internet has broadened the channels of dissemination of information,it has also led to the rapid and widespread propagation of rumors,which can have a serious negative impact socially.In this paper,an improved ISR-WV rumor propagation model integrating multichannels is proposed by considering the system’s time delay,and the influence of different channels of propagation on the dynamic process is further analyzed.Moreover,the basic reproduction number R0,rumor-free equilibrium,and rumor-prevailing equilibrium,as well as their stability,are deduced.Then,an optimal control problem with pulse vaccination is designed.Finally,the validity of the model and theoretical results is verified by numerical simulations and a practical application.The results show that the rumor propagation threshold R0 is more sensitive to the rate of the propagation of the information base channel.The shorter the thinking timeτ_(1)required for the ignorant to react after obtaining the information,the larger the final scale of propagation.Under this condition,the time delayτ_(2)spent by a spreader in producing a video is negatively related to the final scale of the propagation;conversely,a longerτ_(1)implies that the person tends to more cognizant,which can suppress the spread of rumors.Under this condition,τ_(2)has little effect on the final scale of propagation.In addition,the results also prove that timely implementation of the pulse vaccination control strategy of popular science education can effectively control the propagation of rumors and reduce their negative impact.

Stability and optimal control for delayed rumor-spreading model with nonlinear incidence over heterogeneous networks

On the multilingual online social networks of global information sharing,the wanton spread of rumors has an enormous negative impact on people's lives.Thus,it is essential to explore the rumor-spreading rules in multilingual environment and formulate corresponding control strategies to reduce the harm caused by rumor propagation.In this paper,considering the multilingual environment and intervention mechanism in the rumor-spreading process,an improved ignorants–spreaders-1–spreaders-2–removers(I2SR)rumor-spreading model with time delay and the nonlinear incidence is established in heterogeneous networks.Firstly,based on the mean-field equations corresponding to the model,the basic reproduction number is derived to ensure the existence of rumor-spreading equilibrium.Secondly,by applying Lyapunov stability theory and graph theory,the global stability of rumor-spreading equilibrium is analyzed in detail.In particular,aiming at the lowest control cost,the optimal control scheme is designed to optimize the intervention mechanism,and the optimal control conditions are derived using the Pontryagin's minimum principle.Finally,some illustrative examples are provided to verify the effectiveness of the theoretical results.The results show that optimizing the intervention mechanism can effectively reduce the densities of spreaders-1 and spreaders-2 within the expected time,which provides guiding insights for public opinion managers to control rumors.

BIFURCATION CONTROL FOR A FRACTIONAL-ORDER DELAYED SEIR RUMOR SPREADING MODEL WITH INCOMMENSURATE ORDERS

A fractional-order delayed SEIR rumor spreading model with a nonlinear incidence function is established in this paper,and a novel strategy to control the bifurcation of this model is proposed.First,Hopf bifurcation is investigated by considering time delay as bifurcation parameter for the system without a feedback controller.Then,a state feedback controller is designed to control the occurrence of bifurcation in advance or to delay it by changing the parameters of the controller.Finally,in order to verify the theoretical results,some numerical simulations are given.

Predicting abnormal trading behavior from internet rumor propagation:a machine learning approach

In 2021,the abnormal short-term price fluctuations of GameStop,which were triggered by internet stock discussions,drew the attention of academics,financial analysts,and stock trading commissions alike,prompting calls to address such events and maintain market stability.However,the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals.Furthermore,data mining methods are less often used to predict stock trading despite their higher accuracy.This study adopts an innovative approach using social media data to obtain stock rumors,and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior.Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior.The study serves as an impetus for further research using data mining as a method of inquiry.

Deterministic and Stochastic Analysis of a New Rumor Propagation Model with Nonlinear Propagation Rate in Social Network

This paper presents a study on a new rumor propagation model with nonlinear propagation rate and secondary propagation rate. We divide the total population into three groups, the ignorant, the spreader and the aware. The nonlinear incidence rate describes the psychological impact of certain serious rumors on social groups when the number of individuals spreading rumors becomes larger. The main contributions of this work are the development of a new rumor propagation model and some results of deterministic and stochastic analysis of the rumor propagation model. The results show the influence of nonlinear propagation rate and stochastic fluctuation on the dynamic behavior of the rumor propagation model by using Lyapunov function method and stochastic related knowledge. Numerical examples and simulation results are given to illustrate the results obtained.

Mathematical Model of the Dynamics of Rumor Propagation

In this study, we proposed a deterministic mathematical model that attempts to explain the propagation of a rumor using epidemiological models approach. The population is divided into four classes which consist of ignorant individuals, I(t), spreaders targeting community through media, M(t), spreaders targeting community through verbal communication, G(t) and stiflers, R(t). We explored existence of the equilibria and analyzed its stability. It was established that rumour-free equilibrium E0 is locally asymptotically stable if R0<1;meaning rumor can seize spreading in a population, and unstable if R0>1 leads to new rumor spreading in the population. Numerical simulations of the dynamic model are carried out on the system to confirm the analytical results. We see that the dynamics of rumor spreading show similar behavior to that found in the dynamics of infectious diseases except that the spread depends on the classes of spreader.

Dynamics of a New Rumor Propagation Model with the Spread of Truth

A mathematical model described the propagation of information including rumor and truth presented and its properties investigated. We explored exists of the equilibria, local stability and global asymptotical stability, and obtained the propagation threshold of rumor spreading. Numerical simulation is shown to demonstrate our results. Uncertainty and sensitivity analysis shows the importance of the parameters in our model.

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

Research on the Communication Mechanism of Online Rumors Under the Empowerment of New Media Technology-Take the“Nabobess Having an Affair With Courier”Rumor Incident as an Example

As new media communication becomes popular and the technological threshold of personal expression has been lowered,the phenomenon of online rumors has also emerged with new communication characteristics as individuals are continuously empowered to communicate.This paper quantifies a rumor spreading event from 2020 to 2021 that generated a lot of discussion.Using a content analysis approach,we examined how different subjects in the online public opinion chaos have influenced the spreading process of the event,and used the study of online rumor spreading mechanism to gain insight into the public opinion governance in the new media era.The study found that media platforms,the parties involved,and the general public as important participants have fully utilized the voice channel of the online communication platform to play a role behind the rumor event.

Gnutella网络中Rumor Mongering协议的改进

资源搜索是P2P网络的关键问题。目前P2P网络中资源搜索有多种机制，按照资源搜索机制的不同有多种网络模型。其中采用洪泛机制的Gnutella网络模型最接近纯粹的P2P网络，但是Gnutella中资源搜索所采用的洪泛机制因为消息过多冗余导致效率低下而遭到众多质疑。基于这个原因，文中对采用洪泛机制的Gnutella协议做了改进，给出了改进后的协议，实验证明它比Gnutella协议具有明显的优越性。

Correlation and trust mechanism-based rumor propagation model in complex social networks

In real life, the rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, the mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies.The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.

Dynamics of organizational rumor communication on connecting multi-small-world networks

We study the dynamics of an epidemic-like model for the spread of a rumor on a connecting multi-small-world- network （CM-SWN） model, which represents organizational communication in the real world. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the three groups under two evolution rules, namely the conformity and obeying principles. The variant of a dynamic CM-SWN, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals in a single network and stable connection by star nodes between networks, is also analysed in detail and compared with a mean-field approximation.

The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network

In this work, a rumor’s spreading and controlling in a directed Micro-blog user network being consisted with 580 000 nodes are simulated. By defining some authority nodes that release anti-rumor information as the prevention strategy, the effect of the nodes’ role in network on rumor’s suppression is studied. The findings show that rumor will be spread out fast and reach a stable level within limited steps. The suppression of rumor is more predominated by the intervening opportunity, the earlier the intervention strategy was implemented, the better the rumor’s controlling could be achieved. The controlling effect is less relevant with the role of the authority nodes in network.

Dynamics of a stochastic rumor propagation model incorporating media coverage and driven by Lévy noise

With the development of information technology,rumors propagate faster and more widely than in the past.In this paper,a stochastic rumor propagation model incorporating media coverage and driven by Lévy noise is proposed.The global positivity of the solution process is proved,and further the basic reproductive number R_(0) is obtained.When R_(0)<1,the dynamical process of system with Lévy jump tends to the rumor-free equilibrium point of the deterministic system,and the rumor tends to extinction;when R_(0)>1,the rumor will keep spreading and the system will oscillate randomly near the rumor equilibrium point of the deterministic system.The results show that the oscillation amplitude is related to the disturbance of the system.In addition,increasing media coverage can effectively reduce the final spread of rumors.Finally,the above results are verified by numerical simulation.

Research on the Prevention and Control of the Internet Rumor from the Perspective of the Self-Media

With the rapid development of Internet science and technology, the self-media industry is rising gradually. As an important way of information dissemination, more and more self-media platforms are established and the main body of information communication becomes more complex. The self-media not only brings convenience to people’s life but also brings some negative effects. The self-media has more remarkable characteristics in information dissemination. The birth of self-media makes the network appear more suspicious information that cannot be effectively verified. Internet rumors fly all over the sky, which has caused certain influence on the stability of the society. The prevention and control measures of online rumors from the perspective of self-media are studied in this paper for creating a healthier network environment. Firstly, the concepts of self-media and Internet rumors are briefly summarized. Then, the main characteristics of Internet rumors from the perspective of self-media are analyzed. Finally, the prevention and control measures of online rumors, including strengthening supervision, improving the quality of self-media, and strengthening public identification of rumors, are proposed.

Social Network Rumor Recognition Based on Enhanced Naive Bayes

In recent years,with the increasing popularity of social networks,rumors have become more common.At present,the solution to rumors in social networks is mainly through media censorship and manual reporting,but this method requires a lot of manpower and material resources,and the cost is relatively high.Therefore,research on the characteristics of rumors and automatic identification and classification of network message text is of great significance.This paper uses the Naive Bayes algorithm combined with Laplacian smoothing to identify rumors in social network texts.The first is to segment the text and remove the stop words after the word segmentation is completed.Because of the data-sensitive nature of Naive Bayes,this paper performs text preprocessing on the input data.Then a naive Bayes classifier is constructed,and the Laplacian smoothing method is introduced to solve the problem of using the naive Bayes model to estimate the zero probability in rumor recognition.Finally,experiments show that the Naive Bayes algorithm combined with Laplace smoothing can effectively improve the accuracy of rumor recognition.

Dynamical behavior and optimal impulse control analysis of a stochastic rumor spreading model

The Internet era has brought great convenience to our life and communication.Meanwhile,it also makes a bunch of rumors propagate faster and causes even more harm to human life.Therefore,it is necessary to perform effective control mechanisms to minimize the negative social impact from rumors.Thereout,firstly,we formulate a rumor spreading model considering psychological factors and thinking time,then,we add white noise(i.e.,stochastic interference)and two pulse control strategies which denote education mechanism and refutation mechanism into the model.Secondly,we obtain the global positive solutions and demonstrate the global exponential stability of the unique positive periodic rumor-free solution.Thirdly,we discuss the extinction and persistence of rumor.Moreover,we use Pontriagin’s minimum principle to explore the optimal impulse control.Finally,several numerical simulations are carried out to verify the effectiveness and availability of the theoretical analysis.We conclude that the pulse control strategies have a great influence on controlling rumor spreading,and different control strategies should be adopted under different transmission scenarios.

Steady-State Analysis of SECIR Rumor Spreading Model in Complex Networks

In this paper, the SECIR rumor spreading model is formulated and analyzed, in which the social education level and the counterattack mechanism are taken into consideration. The results show that improving education level and increasing the ratio of counter are effective in reducing the risk of rumor propagation and enhancing the resistance to rumor propagation.