Dynamic of interactive model for information propagation across social networks media

Abstract

Controlling information diffusion or propagation through social networks can be challenging when dealing with information related to a subject of highest interest for the public. The complexity level of control depends on subject importance, users’ dynamic, and network structure. When two published messages or pieces of information share the same interest for targeted readers, analyzing their propagation dynamic for control and prediction is of great interest. This article proposes to model, based on a modified interactive system with Holling type functional response, the dynamic of underlying relationship between two broadcasted messages traveling through social networks media. We showed in the qualitative analysis of the proposed model that system could be stable at certain conditions, and the model-system exhibited very rich dynamical behavior. Numerical simulation results validated theoretical analyses and suggested adapting resources harvesting and assimilation efficiency for an authoritative message to stabilize the system and control the dissemination of information in a closed environment.

Analysis of the Evolution of User Emotion and Opinion Leaders’ Information Dissemination Behavior in the Knowledge Q&A Community during COVID-19

“…As shown in 2, in the opinion leader information exchange network, certain individuals (4, 14, 54, 79, 110, 140, 166, 186) have a larger in-degree and are therefore good at receiv formation from other users. Some individuals (27,35,54,58,63,117,139,140,14…”

“…Meanwhile, Freeman's betweenness measures the degree of control of resources by actors. Those individuals (14,27,29,35,39,54,58,63,110,117,140,147,152,166,169,173,187) with larger Freeman's betweenness are the middlemen of the network; they have high social proficiency within the topic community and influence the dissemination of information and knowledge. Many individuals in the network need these "social influencers" to connect with other individuals.…”

“…Freeman's betweenness indicated that there were structural holes in the network. By analyzing structural hole-related indicators, we found that certain individuals (14,27,35,44,54,58,63,110,117,140,147,166,169,173,174,187,217) were located in the structural hole of the network and were the middlemen of the network. Individuals at such nodes can also connect two areas to obtain nonredundant information in the network.…”

“…Many studies have investigated information dissemination on social media. In terms of information dissemination networks, many studies have focused on the main body of public opinion [14]. Adopting information dissemination theory and motivation theory, some scholars found that emotional motivation, the social motivation of the information received, and the credibility of the information source had positive effects on users' communication intentions; meanwhile, the emotional tendency of information had a negative effect [15].…”

Dynamics of a Delayed Interactive Model Applied to Information Dissemination in Social Networks

“…To strengthen the authoritative information competitiveness, publishing it at the right time may result in influencing significantly the persistent fake news by reducing its diffusion in the short-or long-term scale. In the proposed model, we introduced a delay parameter in the interaction terms and a control parameter in the prey species equation to control its speed of diffusion and promote the released true news or information [23][24][25][26][27][28][29][30][31][32][33].…”