Modeling and Simulating Online Panic in an Epidemic Complexity System: An Agent‐Based Approach

Abstract

Following the outbreak of a disease, panic often spreads on online forums, which seriously affects normal economic operations as well as epidemic prevention procedures. Online panic is often manifested earlier than in the real world, leading to an aggravated social response from citizens. This paper conducts sentiment analysis on more than 80,000 comments about COVID-19 obtained from the Chinese Internet and identifies patterns within them. Based on this analysis, we propose an agent-based model consisting of two parts—a revised SEIR model to simulate an offline epidemic and a scale-free network to simulate the Internet community. This model is then used to analyze the effects of the social distancing policy. Assuming the existence of such a policy, online panic is simulated corresponding to different informatization levels. The results indicate that increased social informatization levels lead to substantial online panic during disease outbreaks. To reduce the economic impact of epidemics, we discuss different strategies for releasing information on the epidemic. Our conclusions indicate that announcing the number of daily new cases or the number of asymptomatic people following the peak of symptomatic infections could help to reduce the intensity of online panic and delay the peak of panic. In turn, this can be expected to keep social production more orderly and reduce the impact of social responses on the economy.

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

“…Other papers also investigated the effect of some factors, including social media and individual behaviours (Du et al, 2021 ; Palomo‐Briones et al, 2021 ; Zhang et al, 2022 ), fear‐driven behaviours (Rajabi et al, 2021 ), human activity patterns (Wang et al, 2021 ), the impact of cross‐reactivity induced by exposure to endemic human coronaviruses (eHCoVs) (Pinotti et al, 2021 ), natural disasters (de Vries & Rambabu, 2021 ) and misinformation diffusion (Prandi & Primiero, 2020 ). In addition, one article simulated transmission of the virus, and online panic and its adverse effects on the control and prevention of COVID‐19 outbreak (Guo, Li, et al, 2021 ). Another one study explored the relationship between the spread of COVID‐19 and economic activities (Kano et al, 2021 ).…”

Coronavirus statistics causes emotional bias: a social media text mining perspective

“…The consequences of text sentiment analysis may be more objective if there is sufficient data. Traditional methods of sentiment analysis on social media text are either dictionary-and rule-based methods [14,15,16], or shallow models that focus on carefully designed effective features [17], which are tailored to obtain a satisfactory classification of opinion polarities. As for sentiment analysis technology, deep learning methods function better than traditional methods such as the emotional dictionary.…”