Rumor spreading model with considering debunking behavior in emergencies

Tian, Yong; Ding, Xuejun

doi:10.1016/j.amc.2019.124599

Cited by 53 publications

(49 citation statements)

References 37 publications

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“…A competitive diffusions of rumor and knowledge [29] was discussed, in which the diffusion of knowledge is the key to controlling rumor. And a rumor spreading model called ILRDS [30] was proposed to describe the rumor dynamics considering the debunkers who prey on rumor spreaders. Based on the above analysis, these two articles only discuss one aspect of relationship between rumor and rumor-refuting information respectively.…”

Section: ) the Relationship Of Populationsmentioning

confidence: 99%

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

Zhang

2021

IEEE Access

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The control of rumors spreading revolves around the interplay of the rumors and rumorrefuting information. Rumor-refuting information preys on the rumors, and competes with rumors for the same resource, netizens. There's both a predatory and competitive relationship between the rumors and rumor-refuting information. In this paper, we present a simple model to describe the interplay between rumors and rumor-refuting information based on biomathematics theory. By drawing from differential equations, A theoretical analysis reveals that this model exhibits three dynamic cases; extinction of rumors, extinction of rumor-refuting information and coexistence. And we study the stability of the equilibrium points of three cases, find that stable condition of equilibrium point, and show unstable case of model. Data from a micro-blog rumor about haze and the official micro-blog refuting this rumor were then crawled and the spreading process analyzed, and the haze rumors and rumor-refuting information evolution is estimated and forecasted by applying the mixture model. The integral method is deployed for the estimation of the model's parameters. Findings suggest that the rumors and the rumor-refuting information coexist in the case. and this case study is consistent with the theoretical analysis of coexistence. Based on this case, several scenarios were suggested to more deeply analyze the impact of the the effect of the authority's credibility and public cognition through the model. Finally, some policy suggestion for authorities that can contribute to rumor management in an emergency event.

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Section: ) the Relationship Of Populationsmentioning

confidence: 99%

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

Zhang

2021

IEEE Access

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“…The reason is that new things and mass emerging information on social media can easily divert users' attention from a specific event. Moreover, refutation information may also affect the decision of user spreading rumor [32]. For example, if a user has known and accepted the truth, he/she will not retweet the relevant rumor.…”

Section: ) Experiments Ea+1mentioning

confidence: 99%

Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

et al. 2020

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Rumors in social media not only affect the health of online social networks, but also reduce the quality of information accessed by social media users. When emergencies occur, the rapid spread of rumors can even trigger mass anxiety and panic. However, the existing studies did not make a clear distinction between rumor and non-rumor information in public emergencies, so that they cannot effectively predict the rumor retweeting behavior. To this end, a model for predicting rumor retweeting behavior is presented based on the convolutional neural networks (CNN) called R-CNN model in the paper. In this model, the rumor retweeting behavior is considered as an important driving force of increasing the depth and breadth of rumor cascades, and four feature vectors are constructed with the historical textual content published by users, consisting of attention to public emergencies, attention to rumors, reaction time and tweeting frequency. To input the quantitative feature vectors for R-CNN, a K-means based core tweets extraction method is proposed to select the right tweets, and the quantitative feature representations are proposed. The predictive capability of the model has been proved by experiments base on two rumor datasets of emergencies crawled from Sina weibo. Experimental results indicate that the prediction accuracy of the model reaches 88%, and it can be improved by 7% on average compared with other models. INDEX TERMS Behavior prediction, convolutional neural networks, public emergency, rumor.

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“…Yao improved SIR model to SDILR (susceptible–dangerous–infective–latent–recovered) model by considering the filtering function of social media for rumors [ 10 ]. In addition, many workers have also established new models based on specific situations in real life [ 8 , 11 – 14 ]. For example, Tian assumed that ignorants had three different attitudes when facing rumors, and thus, they established a new SDILR model [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, many workers have also established new models based on specific situations in real life [ 8 , 11 – 14 ]. For example, Tian assumed that ignorants had three different attitudes when facing rumors, and thus, they established a new SDILR model [ 8 ]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Spatial dynamics and optimization method for a rumor propagation model in both homogeneous and heterogeneous environment

et al. 2021

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Considering the influence of environmental capacity and forgetting on rumor spreading, we improve the traditional SIR (susceptible–infected–removed) rumor propagation model and give two dynamic models of rumor propagation in heterogeneous environment and homogeneous environment, respectively. The main purpose of this paper is to make a dynamic analysis of rumor propagation models. In the spatial heterogeneous environment, we have analyzed the uniform persistence of the rumor propagation model and the asymptotic behavior of the positive equilibrium point when the diffusion rate of rumor–susceptible tends to zero. In the spatial homogeneous environment, we discuss the stability of rumor propagation model. Further, optimal control and the necessary optimality conditions are obtained by using the maximum principle. Finally, we study the Hopf bifurcation phenomenon through inducing time delay in the reaction–diffusion model. In addition, the existence of Hopf bifurcation is verified and the influence of diffusion coefficients is studied by numerical simulations.

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Rumor spreading model with considering debunking behavior in emergencies

Cited by 53 publications

References 37 publications

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

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

Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

Spatial dynamics and optimization method for a rumor propagation model in both homogeneous and heterogeneous environment

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