Information filtering by smart nodes in random networks

Exploring agent interaction patterns in the comment sections of fake and real news

User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user reactions. However, the interactions among agents in the comment sections for fake and real news have not been fully explored. In this study, we analyse a dataset comprising both fake and real news from Reddit to investigate agent interaction patterns, considering both the network structure and the sentiment of the nodes. Our main findings reveal that, compared with fake news, where users generate more negative sentiment, real news tends to elicit more neutral and positive sentiments. Additionally, nodes with similar sentiments cluster together more tightly than anticipated. From a dynamic perspective, we found that the sentiment distribution among nodes stabilizes early and remains stable over time. These findings have both theoretical and practical implications, particularly for the early detection of real and fake news within social networks.

Local-Forest Method for Superspreaders Identification in Online Social Networks

Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.

Role of lurkers in threshold-driven information spreading dynamics

The threshold model as a classical paradigm for studying information spreading processes has been well studied. The main focuses are on how the underlying social network structure or the size of initial seeds can affect the cascading dynamics. However, the influence of node characteristics has been largely ignored. Here, inspired by empirical observations, we extend the threshold model by taking into account lurking nodes, who rarely interact with their neighbors. In particular, we consider two different scenarios: (i) Lurkers are absolutely silent and never interact with others and (ii) lurkers intermittently interact with their neighborhood with an activity rate p. In the first case, we demonstrate that lurkers may reduce the effective average degree of the underlying network, playing a dual role in spreading dynamics. In the latter case, we find that the stochastic dynamic behavior of lurkers could significantly promote the spread of information. Concretely, slightly raising the activity rate p of lurkers may result in a remarkable increase in the final cascade size. Further increasing p could make nodes become more stable on average, while it is still easy to observe global cascades due to the fluctuations of the effective degree of nodes.

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R_{0} has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

The impact of malicious nodes on the spreading of false information

Increasing empirical evidence in recent years has shown that bots or malicious users in a social network play a critical role in the propagation of false information, while a theoretical modeling of such a problem has been largely ignored. In this paper, applying a simple contagion model, we study the effect of malicious nodes on the spreading of false information by incorporating the smart nodes who perform better than normal nodes in discerning false information. The malicious nodes, however, will always repost (or adopt) the false message as long as they receive it. We show analytically that, for a random distribution of malicious nodes, there is a critical number of malicious nodes above which the false information could outbreak in a random network. We further study three different distribution strategies of selecting malicious nodes for false information spreading. We find that malicious nodes that have large degrees, or are tightly connected, can enhance the spread. However, when they are close to the smart nodes, the spreading of false information can either be promoted or inhibited, depending on the network structure.

Totally asymmetric simple exclusion process on multiplex networks

We study the totally asymmetric simple exclusion process on multiplex networks, which consist of a fixed set of vertices (junctions) connected by different types of links (segments). In particular, we assume that there are two types of segments corresponding to two different values of hopping rate of particles (larger hopping rate indicates particles move with higher speed on the segments). By simple mean-field analysis and extensive simulations, we find that, at the intermediate values of particle density, the global current (a quantity that is related to the number of hops per unit time) drops and then rises slightly as the fraction of low-speed segments increases. The rise in the global current is a counterintuitive phenomenon that cannot be observed in high or low particle density regions. The reason lies in the bimodal distribution of segment densities, which is caused by the high-speed segments.