Using SIR Model to Simulate Emotion Contagion in Dynamic Crowd Aggregation Process

Abstract

Emotion contagion is an indispensable behavior in a dynamic crowd, especially in an evacuation situation. As a consequence, generating emotion contagion results is very useful in the crowd simulation field. However, because the topology of the crowd usually keeps changing dynamically, computing the contagion process is a challenge. In this paper, we represented our research about the emotion contagion effects on the virtual pedestrian dynamic aggregation process. First of all, we calculated individuals' moving parameters based on their prefixed expectations according to the social force theory. After this, we made an adjacent test for each individual to generate nearer neighbors for further emotional contagions computing between neighbors. We then treated the emotional contagions between individuals and their neighbors as the information spreading process so that we can adopt the emotional information spreading model SIR (Susceptible Infective Removal) to calculate emotional influences, which are represented as their changing moving velocities during aggregation. Social force for computing low level moving parameters and SIR model for generating emotional influences were integrated by our method to simulate the dynamic pedestrian aggregation. Experimental results showed that the SIR model can effectively improve the fidelity of the emotional interaction process and crowd aggregation.

What makes groups emotional?

“…While bottom up attempts to predict collective emotions have been relatedly sparce, there are many computational that attempt to capture collective emotions (Bosse et al, 2014;Fan et al, 2018;Gao & Liu, 2017;Garcia et al, 2011;Haeringen et al, 2021;Hill et al, 2010;Riahi, 2015;Wang et al, 2015;Xiang et al, 2018;Xu et al, 2021). These models are built with the hope that they could provide preliminary signals for increase in collective emotion or maybe even be fitted to actual data in ways that that would help predict the unfolding of collective emotion.…”

Rumor Transmission in Online Social Networks Under Nash Equilibrium of a Psychological Decision Game

“…In the 1920s, Kermack and McKendrick (1927) first modeled the infection dynamics of a disease, whereas the population is divided into three compartments: the susceptible, the infected and the recovered (SIR). The SIR model describes the state dynamics of nodes in a general system, and it sees adoption in a variety of application contexts, such as data transmissions in mobile Inter-vehicular communication networks (Mashwama et al 2020), and dynamic emotion contagion in a crowd of evacuees (Xiang et al 2018). After that, the development of various epidemiological models and mechanisms subsequently emerged, often assuming a well-mixed population that can be described by an SIR-type system dynamics (Daley and Kendall 1964;Maki and Thompson 1973;Sudbury 1985;Rey et al 2016).…”

Virus Propagation in Wireless Sensor Networks with Media Access Control Mechanism

“…As far as we know, the original model and improved version of SIR are applicable to not only WSN networks, but also other complex networks, and the scope of application is quite extensive. For example, Xiang et al [23] used SIR model to simulate emotion contagion in dynamic crowd aggregation process, and they found that the SIR model can effectively improve the fidelity of emotional interaction processes and crowd aggregation. Lamb et al [24] described the SIR stochastic epidemic model of computer virus by combining the time-Markov chain of the minimum traffic model and the control of virus propagation.…”