Abstract
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.
Highlights
Rumors are a common human phenomenon, the transmission of which have exponentially grown since the development of new instant social media platforms such as Facebook, Twitter, and microblogs
EMPIRICAL ANALYSIS AND RESULTS a case is given to describe the competition and predation behaviors between rumor propagation and rumor refutation, each of which is represented by the parameter values and dynamic model analysis
PARAMETER ANALYSIS The effects of parameters b1 and b2 on rumor propagation were examined, where b1> 0 is the efficiency at which the rumor spreaders convert the unaware population into rumor spreaders, and b2 > 0 is the efficiency at which the rumor refutation spreaders convert the unaware population into rumor refutation spreaders
Summary
Rumors are a common human phenomenon, the transmission of which have exponentially grown since the development of new instant social media platforms such as Facebook, Twitter, and microblogs. People can receive and publish any information they want at any time anywhere. Because a great deal of network communication is anonymous, people are more willing to give their own views regardless of the truth, which has led to an increase in the number of unconfirmed rumors spreading across networks, which in turn can affect the direction of public opinion and government credibility. Rumor propagation in public emergencies in particular can lead to panic and societal instability [1]. Governments or the relevant authorities commonly release rumor-refutations to guide and control public opinion.
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