Rumor Propagation Research Articles

Stability Analysis of a Delayed Rumor Propagation Model with Nonlinear Incidence Incorporating Impulsive Vaccination.

The presence of information asymmetry can hinder the public's ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered a periodic impulsive intervention to mitigate the impact of information asymmetry and promote a more informed and discerning public. Drawing on these findings, this paper proposes a susceptible-hesitant-infected-refuting-recovered (SHIDR) rumor-spreading model to explain the spread of rumors. The model incorporates elements such as time delay, nonlinear incidence, and refuting individuals. Firstly, by applying the comparison theorem of an impulsive differential equation, we calculate two thresholds for rumor propagation. Additionally, we analyze the conditions of global attractiveness of the rumor-free periodic solution. Furthermore, we consider the condition for the rumor's permanence. Finally, numerical simulations are conducted to validate the accuracy of our findings. The results suggest that increasing the proportion of impulsive vaccination, reducing the impulsive period, or prolonging the delay time can effectively suppress rumors.

Optimal control of a rumor propagation model in online social network by considering influential nodes

Social networking platforms give people an opportunity to get in contact with each other irrespective of boundaries. Through these platforms, any individual can easily follow the ideas of other individuals like friends, relatives, politicians, actors, etc, and get in contact with them. In this paper, we examine rumor propagation with two types of infected people: highly influential people and ordinary people. Some ordinary people, blindly follow the messages shared by highly influential people. By using this concept we analyze the rumor-spreading model in networks (homogeneous and heterogeneous). We applied control strategies in homogeneous networks and observed that government policies to control rumor propagation can reduce its spread. Immunization in heterogeneous networks by giving proper information and education to people about facts can also reduce the spread and help people make the right decision when they come across a rumor.

BiMGCL: rumor detection via bi-directional multi-level graph contrastive learning

The rapid development of large language models has significantly reduced the cost of producing rumors, which brings a tremendous challenge to the authenticity of content on social media. Therefore, it has become crucially important to identify and detect rumors. Existing deep learning methods usually require a large amount of labeled data, which leads to poor robustness in dealing with different types of rumor events. In addition, they neglect to fully utilize the structural information of rumors, resulting in a need to improve their identification and detection performance. In this article, we propose a new rumor detection framework based on bi-directional multi-level graph contrastive learning, BiMGCL, which models each rumor propagation structure as bi-directional graphs and performs self-supervised contrastive learning based on node-level and graph-level instances. In particular, BiMGCL models the structure of each rumor event with fine-grained bidirectional graphs that effectively consider the bi-directional structural characteristics of rumor propagation and dispersion. Moreover, BiMGCL designs three types of interpretable bi-directional graph data augmentation strategies and adopts both node-level and graph-level contrastive learning to capture the propagation characteristics of rumor events. Experimental results on real datasets demonstrate that our proposed BiMGCL achieves superior detection performance compared against the state-of-the-art rumor detection methods.

A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms

In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generative models such as GPT and diffusion models, have facilitated the propagation of rumors, posing challenges to society. Detecting and countering these rumors to mitigate their adverse effects on individuals and society is imperative. Automatic rumor detection, typically framed as a binary classification problem, predominantly relies on supervised machine learning models, necessitating substantial labeled data; yet, the scarcity of labeled datasets due to the high cost of fact-checking and annotation hinders the application of machine learning for rumor detection. In this study, we address this challenge through active learning. We assess various query strategies across different machine learning models and datasets in order to offer a comparative analysis. Our findings reveal that active learning reduces labeling time and costs while achieving comparable rumor detection performance. Furthermore, we advocate for the use of machine learning models with nonlinear classification boundaries on complex environmental datasets for more effective rumor detection.

Influence of Social Media on the Behavioral Formation of Digital Natives: a Moderation Approach

Purpose: This study examined the influence of social media on the behavioural formation of digital natives in Lagos, Nigeria, with a specific focus on the accessibility and pattern of social media usage, the online experience, and the influence of social media on the behavioural formation of digital natives. Design/Methodology/Approach: Using a stratified proportionate sample strategy, 450 social media users who grew up in the digital age across 10 local government areas in Lagos, Nigeria, provided the data for this quantitative study, where the heuristic model for the relationship was subjected to a string of tests using the partial least squares structural equation modelling (PLS-SEM) technique. Findings: Discovery revealed that social media has a favourable impact on the behavioural formation of digital natives, whereby digital natives’ nature moderates the structural relationship between social media and behavioural formation. Original/Value: The study novelty appears on the side of Technological Determinism Theory postulated on social media as extremely popular among Nigerian digital natives and is one of the key contributors to the deterioration of digital natives’ behavioural formation in terms of morality, secretive information accessing, online insecurity, as well as the propagation of rumor and propaganda. Practical implications: Because of the established dialectical connection between society and technology, widespread social media literacy is now possible, even among the humblest of households.

Ternary interaction evolutionary game of rumor and anti-rumor propagation under government reward and punishment mechanism

Ternary interaction evolutionary game of rumor and anti-rumor propagation under government reward and punishment mechanism

MODELING THE SPREAD OF FAKE NEWS ON SOCIAL NETWORKING SITES USING THE SYSTEM DYNAMICS APPROACH

The problem of false news online has continued to worsen, especially after witnessing significant events around the world unfold, such as the 2018 Cambridge Analytica scandal, COVID-19 pandemic, to the 2021 January 6th Insurrection at the US Capitol. False information online has distorted online users’ perception of the real world. As daily life is more intertwined with the digital world, false news becomes a more urgent concern because of the way it can shape public opinion. This study presents a rumor propagation model, which was based on epidemiological models, to address the spread of false news on social networking sites. The existing model was expanded on the STELLA software to consider the cognitive process of users when encountering false news, the platform in which the false news spreads, and the relationship of false news with online users. Simulations showed that Confirmation Bias, Sharing of Posts, and Algorithmic Ranking were the three critical variables of the model. It was found that possible interventions include a mix of reducing the bias of users at a wide-scale level and restructuring the SNS algorithm.

Dynamical behaviors and optimal control of delayed S2IS rumor propagation model with saturated conversion function over complex networks

Nowadays, online information is disseminated in multiple languages on different platforms, and research on the dissemination trend and control of online rumors is a topic worth discussing. In the bilingual environment, we study the S2IS rumor propagation model with time delay and saturated conversion function which implies the limitation of resources in homogeneous and heterogeneous networks. First and foremost, the analysis of the boundedness of the solutions and the existence of the rumor-spreading equilibrium points is given in our work, and we figure out the basic reproduction number. Next, we conduct the local stability analysis of the rumor-free equilibrium point and the rumor-spreading equilibrium points according to the characteristic equation. The global stability of the rumor-free equilibrium point is shown based on the Lyapunov stability theory. Besides, we study the forward and backward bifurcation phenomenon and optimal control problem in theory, which is seldom discussed in the bilingual environment. Finally, numerical simulations are performed to verify the correctness of our main results.

Graph Convolutional Network-Based Rumor Blocking on Social Networks

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms.

Simplicial SIR rumor propagation models with delay in both homogeneous and heterogeneous networks

Simplicial SIR rumor propagation models with delay in both homogeneous and heterogeneous networks

Modeling the competitive propagation of rumors and counter-rumors in two-layer complex networks with nonlinear spreading and decay rates

Prior investigations into the propagation of rumors have predominantly focused on either online or offline social networks, neglecting the concurrent dissemination of rumors and counter-rumors. Hence, to investigate how rumors and counter-rumors spread and interact in online and offline two-layer networks, and to devise an optimal control strategy for rumors, we propose a nonlinear SEIDR spreading model that incorporates the effects of social reinforcement and interest decay. By constructing mean-field equations, we present the mathematical expression of the SEIDR model and further define and compute the basic reproduction number of rumors. Subsequently, three theorems related to the final scale of rumors are introduced. Finally, we investigate the primary factors influencing rumor propagation via Monte Carlo simulations, yielding the following key findings: (i) The final scale of rumors exhibits a positive correlation with the social reinforcement effect and the second-order moment of the network, while displaying a negative correlation with the interest decay effect and the average degree of the network. (ii) The final scale of rumors ranks in the order of: random network > scale-free network > small-world network > regular network. (iii) Notably, the speed of rumor propagation aligns with the order: scale-free network > random network > small-world network > regular network.

Lyapunov functions for studying global asymptotic stability of two rumor spreading models

In a previous work (2018, Commun. Theor. Phys. 70, 795–802), a new compartment model for the spreading of rumors was introduced and analyzed. However, only the local asymptotic stability of this model was discussed. In the present work, we first provide a rigorous mathematical analysis for the global asymptotic stability (GAS) of the above-mentioned rumor spreading model. By constructing suitable Lyapunov candidate functions, we obtain the GAS of a rumor-free (boundary) equilibrium point and a unique rumor-spreading (positive) equilibrium point. After that, we utilize the approach based on the Lyapunov candidate functions to study the GAS of another rumor spreading model with control strategies, which was proposed in (2022, Physica A 606, 128157). As an important consequence, the GAS of the rumor spreading model with control strategies is determined fully without resorting to technical hypotheses used in the benchmark work. Lastly, the theoretical findings are supported by a set of illustrative numerical examples. The obtained results not only improve the ones constructed in the two above-mentioned benchmark papers but also can be extended to study the global dynamics of other rumor propagation models in the context of both integer-order and fractional-order derivatives.

The Attitudes of Umm Al-Qura University Students towards Rumors Spreading on Online Social Networks During Covid Pandemic

The current study aimed to explore the attitudes of Umm Al-Qura University students towards rumor spreading on online social networks during the Covid pandemic. The study utilized survey research methodology, which is one of the most important methods in media research and employed a questionnaire to collect the required data. The results revealed that the majority of the sample spent an average of more than three hours per day on social media platforms. The sampled students agreed that social media networks contribute to the propagation of rumors. Data analysis further revealed a common consensus among the study sample that WhatsApp was the most used medium for spreading rumors during the pandemic, and that the most wide-spread rumors were about vaccine efficacy and safety. Furthermore, the study recommended the implementation of social media awareness campaigns suitable for university students and citizens of all ages, focusing on the dangers of rumor spreading and promoting the effective use of social media networks to seek accurate information.

Online-Offline Higher-Order Rumor Propagation Model Based on Quantum Cellular Automata Considering Social Adaptation

With the development of the Internet, the influence of rumors is more widespread. Studying the propagation law of rumors is an effective way to reduce the damage of rumors. We therefore propose a new two-layer rumor propagation model, which simultaneously introduces simplicial complexes into the online and offline network layers and considers the social adaptation of individuals. We derive the basic regeneration number, the rumor-free equilibrium, and the rumor-prevalence equilibrium using mean-field theory, and verify their accuracy through extensive computer simulations. The results show that the inclusion of higher-order interactions in both layers leads to richer dynamical phenomena. Specifically, higher-order interactions lead to bistable states, discontinuous transitions, and the coexistence of multiple rumor-prevalence equilibria, and increasing the infection parameter within a certain range reduces the outbreak size of rumors in another layer. In addition, a higher speed and degree of social adaptation, a stronger strength of coupled propagation enhancement effect, and a weaker strength of coupled recovery enhancement effect significantly increase the spread speed and size of rumors. Finally, we present and analyze two intervention strategies: improving public media literacy and building a truth space. We find that both intervention strategies can reduce the spread size and speed of rumors, but the synergy of two intervention strategies is more effective.

On a Rumor Propagation Model with Spatial Heterogeneity

On a Rumor Propagation Model with Spatial Heterogeneity

Modeling the dynamics of information propagation in the temporal and spatial environment

In this paper, we try to establish a non-smooth susceptible–infected–recovered (SIR) rumor propagation model based on time and space dimensions. First of all, we prove the existence and uniqueness of the solution. Secondly, we divide the system into two parts and discuss the existence of equilibrium points for each of them. For the left part, we define R 0 to study the relationship between R 0 and the existence of equilibrium points. For the right part, we classify many different cases by discussing the coefficients of the equilibrium point equation. Then, on this basis, we perform a bifurcation analysis of the non-spatial system and find conditions that lead to the existence of saddle-node bifurcation. Further, we consider the effect of diffusion. We specifically analyze the stability of equilibrium points. In addition, we analyze the Turing instability and Hopf bifurcation occurring at some equilibrium points. According to the Lyapunov number, we also determine the direction of the bifurcation. When I = I c , we discuss conditions for the existence of discontinuous Hopf bifurcation. Finally, through numerical simulations and combined with the practical meaning of the parameters, we prove the correctness of the previous theoretical theorem.

Modeling and controlling delayed rumor propagation with general incidence in heterogeneous networks

To explore the rumor propagation dynamics in online social networks (OSNs) and propose the corresponding control strategies, a novel Ignorants-Spreaders-1-Spreaders-2-Removers (I2SR) rumor-spreading model with general incidence is first formulated in heterogeneous networks. This model covers the bilinear incidence and nonlinear incidence, and considers the time delay in the rumor-spreading process, which is universal and practical. We analytically derive the basic reproduction numbers, which determine the existence of rumor-spreading equilibria but also the global dynamics of the model. Moreover, the Lyapunov–LaSalle principle and the graph-theoretic approach prove the global asymptotic stability of the rumor-free/rumor-spreading equilibria in detail. Significantly, the effects of various strategies, including uniform control, targeted control, and acquaintance control, are implemented and compared. Finally, the developed theoretical results are tested via numerical simulations and a real case data of rumors, showing that the model can more accurately simulate rumor-spreading in OSNs.

Rumor propagation model with neutral state

The development of social networks provides a broad platform for the dissemination of information and also leads to the proliferation of fake news and false information, which we collectively refer to as rumors. The spread of rumors causes unnecessary panic and loss to individuals and society. To reduce the negative impacts of rumors, an appropriate rumor control strategy is necessary. To come up with some reasonable strategies, we need to have a clearer understanding of the spread of rumors. In this paper, we analyze crowd attitudes during the spreading of rumors by setting the misinformation prevalent progress on the social network as a dynamic system. Considering that most people do not have a clear supportive or opposing attitude when exposed to rumor information, we introduce a new group, stiflers who remain neutral, based on the infectious disease model scheme. By deriving the mean-field equation describing the rumor propagation process, we judge the stability of the constructed model. Finally, we use the model to fit the real-world data related to COVID-19, and based on this, we discuss the properties of the model and propose related strategies.

Stochastic Growth Models for the Spreading of Fake News

The propagation of fake news in online social networks nowadays is becoming a critical issue. Consequently, many mathematical models have been proposed to mimic the related time evolution. In this work, we first consider a deterministic model that describes rumor propagation and can be viewed as an extended logistic model. In particular, we analyze the main features of the growth curve, such as the limit behavior, the inflection point, and the threshold-crossing-time, through fixed boundaries. Then, in order to study the stochastic counterparts of the model, we consider two different stochastic processes: a time non-homogeneous linear pure birth process and a lognormal diffusion process. The conditions under which the means of the processes are identical to the deterministic curve are discussed. The first-passage-time problem is also investigated both for the birth process and the lognormal diffusion process. Finally, in order to study the variability of the stochastic processes introduced so far, we perform a comparison between their variances.

Stochastic Stabilization of Dual-Layer Rumor Propagation Model with Multiple Channels and Rumor-Detection Mechanism.

With the development of information technology, individuals are able to receive rumor information through various channels and subsequently act based on their own perceptions. The significance of the disparity between media and individual cognition in the propagation of rumors cannot be underestimated. In this paper, we establish a dual-layer rumor propagation model considering the differences in individual cognition to study the propagation behavior of rumors in multiple channels. Firstly, we obtain the threshold for rumor disappearance or persistence by solving the equilibrium points and their stability. The threshold is related to the number of media outlets and the number of rumor debunkers. Moreover, we have innovatively designed a class of non-periodic intermittent noise stabilization methods to suppress rumor propagation. This method can effectively control rumor propagation based on a flexible control scheme, and we provide specific expressions for the control intensity. Finally, we have validated the accuracy of the theoretical proofs through experimental simulations.