Modeling and analysis of rumor propagation in social networks

The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

A rumor reversal model of online health information during the Covid-19 epidemic.

Rumor spreading has been studied for decades, but its underlying mechanism is still under debate, especially for those ones based on internet. By focusing on the spreading data of six typical events on Sina Weibo, we surprisingly find that the spreading of modern news shows some new features, i.e. either explosively fast or slow, depending on the individual events. To understand its mechanism, we present a Susceptible-Accepted-Recovered (SAR) model with both information sensitivity and social reinforcement. By numerical simulations we show that the model can reproduce the main spreading patterns of the six typical events and the spreading speed can be promoted by increasing either the strength of information sensitivity or the social reinforcement. Moreover, we find that the dependence of final accepted size on some key parameters, such as the transmission probability and information sensitivity, can change from continuous to discontinuous when the strength of the social reinforcement is large. An edge-based theory analysis is presented to explain the explosive spreading by information sensitivity and social reinforcement.

A model for both the fast and slow spreadings of sudden events on social networks.

Spread of misinformation on social media: What contributes to it and how to combat it

Controlling rumors is related to the interplay between the rumor and rumor refutation, which compete to attract the unaware. And rumor refutation preys on the rumors, and try to eliminate rumors. Therefore, there is both a predatory and competitive relationship between the rumors and the rumor refutations. This paper presents a model based on biomathematics theory to describe the interplay between rumors and rumor refutations. The theoretical analysis of the differential equations elucidated three dynamic cases: rumor extinction; rumor refutation extinction; and rumor and rumor refutation coexistence. The subsequent analysis of the equilibrium stability in the three cases revealed both equilibrium stability and model instability. The haze rumor and the official haze refutation data were then crawled from Sina microblog, the rumor propagation process analyzed, an integral method employed for the model parameter estimation, and the proposed model used to estimate and forecast the haze rumor and rumor-refutation evolutions. The findings suggested that rumors and rumor refutations coexist, which was consistent with the theoretical analysis of coexistence. Based on this case and to more deeply analyze the effect of an authority’s credibility and the public’s cognition, the proposed model was used to suggest several scenarios, and policy suggestions given to assist authorities better manage rumors in emergency events.

A Dynamic Competition and Predation Model for Rumor and Rumor-Refutation

In the Internet age, rumors bring great panic, research on the mechanism of propagation will help mitigate the bad influence of rumors In this paper, we propose a modified rumor propagation model with consideration of scientific knowledge level and social reinforcement, and derive the mean-field equations that describe the dynamics of rumor propagation process We obtain the rumor propagation threshold, which is closely related to scientific knowledge level and social reinforcement The threshold of rumor propagation is increased by the scientific knowledge level, and the critical threshold of scientific knowledge level is also affected by the rumor propagation probability The results show that rumor propagates more quickly and more widely in people without scientific knowledge, while rumor propagates more slowly and the final size of the rumor is smaller in people with scientific knowledge Positive social reinforcement will reduce the propagation threshold of rumor propagation, increase the propagation rate, and finally increase the rumor scale Negative social reinforcement has the opposite effect on rumor These results are verified by numerical simulations on scale-free networks Research results also provide a good reference for future studies on how to control rumor propagation

Rumor propagation model with consideration of scientific knowledge level and social reinforcement in heterogeneous network

Rumor is defined as an unconfirmed public opinion or gossip, which can be propagated through various channels, causing serious economic losses and disturbing the normal order of life. Therefore, studying the rumor propagation mechanism is helpful to implement effective rumor propagation control strategies. In this paper, the wise man is involved in the dynamic process of rumor propagation, and the modified rumor propagation model is established with consideration of social reinforcement and forgetting factors in heterogeneous network. The theories analysis of the model is carried out based on the mean field equation. The rumor propagation threshold is obtained, which is closely related to dual social reinforcement, wisdom conversion rate and forgetting factors. The final size of the rumor is related to positive social reinforcement and negative social reinforcement. Numerical simulations indicate that, the social positive reinforcement promotes rumor propagation and social negative reinforcement suppresses rumor propagation. And the addition of wise men reduces the number of spreaders, thus affecting the process of rumor propagation.

Sijing Chen

Papers

Rumor propagation model with consideration of scientific knowledge level and social reinforcement in heterogeneous network

Dynamic analysis of the rumor propagation model with consideration of the wise man and social reinforcement

Spread of misinformation on social media: What contributes to it and how to combat it