Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

A study on the competitive dissemination of disinformation and knowledge on social media

Purpose

The purpose of this study is to explore the evolutionary path and stable strategy for the competitive dissemination between disinformation and knowledge on social media to provide effective solutions to curb the dissemination of disinformation and promote the spread of knowledge.

Design/methodology/approach

Based on the social capital (SC) theory, the benefit matrix is constructed and an evolutional game model is established in this paper. Through model solving and Matrix Laboratory (MATLAB) simulation, the factors that influence disinformation-believing users (DUs) and knowledge-believing users (KUs) to choose different strategies are analyzed.

Findings

The initial dissemination willingness, the disinformation infection probability, the knowledge infection probability and the knowledge penetration probability are proved to be crucial factors influencing the game equilibrium in the competitive dissemination process of disinformation and knowledge. Moreover, some countermeasures and recommendations for the governance of disinformation are proposed.

Originality/value

Currently most research interest lies in the disinformation dissemination model but ignores the interaction between disinformation and knowledge in the diffusion process. This study reveals the dynamic mechanism of social media users disseminating disinformation and knowledge and is expected to promote the formation of cleaner cyberspace.

An online multi-dimensional opinion dynamic model with misinformation diffusion in emergency events

Multiple opinions, including many that are negative, are produced in emergency events. These opinions are commonly formed asynchronously based on misinformation. However, most researches on opinion dynamics involving information neglect the asynchronous process of initial opinion formation due to information diffusion. Since online social networks like Sina Weibo act as major avenues for the expression, after analysing online behaviours, an opinion dynamic model is developed with consideration of misinformation diffusion of public opinion. In this model, schemes are developed for opinion interactions in multiple dimensions by introducing characteristics of online communication as another way of opinion interactions besides communication between neighbours. Subsequently, we investigate the impacts of network structure, diffusion rate, repost rate and other factors, which provide insights into understanding online opinion dynamics during emergency events. Furthermore, we conduct simulations to determine the intervention effects of different official responses. Results show that removing comments compulsively exhibits better performance in reducing negative opinion as well as increasing the density of Spreaders. Debunking misinformation by posting early results officially which indicates the probability of the existence of misinformation may lead public opinion in time if it takes a long time to finally confirm the misinformation.

Rumor spreading: A trigger for proliferation or fading away

The dynamics of rumor spreading is investigated using a model with three kinds of agents who are, respectively, the Seeds, the Agnostics, and the Others. While Seeds are the ones who start spreading the rumor being adamantly convinced of its truth, Agnostics reject any kind of rumor and do not believe in conspiracy theories. In between, the Others constitute the main part of the community. While Seeds are always Believers and Agnostics are always Indifferents, Others can switch between being Believer and Indifferent depending on who they are discussing with. The underlying driving dynamics is implemented via local updates of randomly formed groups of agents. In each group, an Other turns into a Believer as soon as m or more Believers are present in the group. However, since some Believers may lose interest in the rumor as time passes by, we add a flipping fixed rate 0<d<1 from Believers into Indifferents. Rigorous analysis of the associated dynamics reveals that switching from m=1 to m≥2 triggers a drastic qualitative change in the spreading process. When m=1, even a small group of Believers may manage to convince a large part of the community very quickly. In contrast, for m≥2, even a substantial fraction of Believers does not prevent the rumor dying out after a few update rounds. Our results provide an explanation on why a given rumor spreads within a social group and not in another and also why some rumors will not spread in neither groups.

Partial differential equation modeling of rumor propagation in complex networks with higher order of organization

Mathematical modeling is an important approach to research rumor propagation in online social networks. Most of prior work about rumor propagation either carried out empirical studies or focus on ordinary differential equation models with only consideration of temporal dimension; little attempt has been given on understanding rumor propagation over both temporal and spatial dimensions. This paper primarily addresses an issue related to how to define a spatial distance in online social networks by clustering and then proposes a partial differential equation model with a time delay to describing rumor propagation over both temporal and spatial dimensions. Theoretical analysis reveals the existence of equilibrium points, a priori bound of the solution, the local stability and the global stability of equilibrium points of our rumor propagation model. Finally, numerical simulations have analyzed the possible influence factors on rumor propagation and proved the validity of the theoretical analysis.

Characterizing super-spreading in microblog: An epidemic-based information propagation model

As the microblogging services are becoming more prosperous in everyday life for users on Online Social Networks (OSNs), it is more favorable for hot topics and breaking news to gain more attraction very soon than ever before, which are so-called "super-spreading events". In the information diffusion process of these super-spreading events, messages are passed on from one user to another and numerous individuals are influenced by a relatively small portion of users, a.k.a. super-spreaders. Acquiring an awareness of super-spreading phenomena and an understanding of patterns of wide-ranged information propagations benefits several social media data mining tasks, such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. Taking into account that super-spreading in both information diffusion and spread of a contagious disease are analogous, in this study, we build a parameterized model, the SAIR model, based on well-known epidemic models to characterize super-spreading phenomenon in tweet information propagation accompanied with super-spreaders. For the purpose of modeling information diffusion, empirical observations on a real-world Weibo dataset are statistically carried out. Both the steady-state analysis on the equilibrium and the validation on real-world Weibo dataset of the proposed model are conducted. The case study that validates the proposed model shows that the SAIR model is much more promising than the conventional SIR model in characterizing a super-spreading event of information propagation. In addition, numerical simulations are carried out and discussed to discover how sensitively the parameters affect the information propagation process.