key: cord-0706121-mmcpcn34 authors: Li, Qianqian; Chen, Tinggui; Yang, Jianjun; Cong, Guodong title: Based on Computational Communication Paradigm: Simulation of Public Opinion Communication Process of Panic Buying During the COVID-19 Pandemic date: 2020-11-20 journal: Psychol Res Behav Manag DOI: 10.2147/prbm.s280825 sha: 72c06a20e55010056d579ad45c85689b767ab1ab doc_id: 706121 cord_uid: mmcpcn34 BACKGROUND AND AIM: The spread of the COVID-19 pandemic has led to a number of instances of large-scale panic buying. Taking the COVID-19 pandemic as an example, this paper explores the impact of panic in uncertain environments on panic buying behavior. Under certain circumstances, the spread of rumors about shortage of goods is likely to cause large-scale panic buying. This paper focuses on the study of such panic buying caused by online rumors. METHODS: Firstly, based on the improved BA network, this paper constructs a directed network for public opinion communication and integrates an offline communication network to build a two-layer synchronous coupling network based on online and offline communications. Secondly, the individual decision model and the panic emotion transmission model under the uncertain environment are constructed. Netizens judge the authenticity of network information, determine their own panic degree according to the above two models, and judge whether they participate in the panic buying based on the above factors. Finally, the spread of the public opinion of goods buying under the panic state is simulated and analyzed. RESULTS: The experimental results of the two-layer synchronous network that integrates offline interaction are significantly different from the results of pure online interaction, which increases the speed of public opinions spread after offline interaction and affects a wider range of groups. Under the condition of sufficient supplies, panic in local areas will not cause large-scale panic buying on the whole network. However, the results under the same parameters suggest that if there is a shortage of supplies, panic will spread quickly across the network, leading to large-scale panic buying. It is very important to ensure sufficient supply of materials at the beginning of the spread of rumors, which can reduce the number of buyers. However, if there is a shortage of goods before the panic dissipates in the later stage, there will still be a large-scale rush purchase. CONCLUSION: These results explain the reasons why it is difficult to stop the buying events in many areas under the COVID-19 pandemic. Under the uncertain environment, the panic caused by people’s fear of stock shortage promotes the occurrence of large-scale rush buying. Therefore, in the event of major public health events, ensuring adequate supply of materials is the top priority. In 2020, the COVID-19 pandemic caused a lot of panic buying around the world. Due to the lack of transparency of information in many countries and regions, people were full of panic or even scared due to uncertain information and then proceeded to hoard goods. 1 People in the United States, Italy, and other countries have hoarded a great number of life supplies, epidemic prevention supplies, and medicine. Even supplies unrelated to epidemic prevention were snapped up in some countries, such as "ShuangHuangLian" medicine in China, granite in Japan, and toilet paper in Italy. During normal times, mass panic buying would disrupt the commodity supply chain and make government management more difficult. 2 Many public health events, such as the COVID-19 pandemic, can easily delay or even disrupt the supply chain, and largescale panic buying events at this time can easily lead to the overall scarcity of social materials. Therefore, it is of great significance to study the current situation and influencing factors of panic buying and explore the deep reasons of panic buying. Panic buying is an emerging phenomenon observed during but not restricted to pandemics. 1 Panic buying generally refers to the abnormal consumption behaviors of a group, 3 which are caused by a specific natural disaster or social event and are concentrated due to the perception of panic under the premise of information opacity. Therefore, they are characterized by uncertainty and panic. 4 Panic behavior during disaster and calamities is an expected response; they threaten the ability to cope, and destroy the existing equilibrium. 5 In panic buying, as a result of social interaction, groups will communicate panic information such as the impending shortage of goods with each other, further interact with their own opinions, and even transmit panic feelings to each other. At the same time, the uncertainty of social information will further deepen the fear of individuals. Therefore, under the influence of herd mentality or panic infection, some individuals tend to hoard materials, which eventually lead to large-scale panic buying events. 6 Panic buying easily disrupts the supply chain and wastes a large amount of resources, greatly harming society. The literature on group buying behavior research usually deals with the analysis of psychological factors or economics explanation, 7 focusing on the meaning of the data but lacking a calculation model. Literature 8 pointed out that only 32 results were obtained by searching literature in Scopus with the keywords "contain panic buying" by early 2020. By studying these documents, it can be found that the current research on panic buying behavior is not complete, only conducting basic study, and has not formed a mature research system. Most of these studies use multiple linear regression or structural equation models to explore the relationship between the target variables, some studies use such qualitative analysis methods as negotiation to study the problem, and a few use quantitative analysis methods. Although these studies can theoretically explain the causes of the panic buying phenomenon, they cannot restore the overall change process of the panic buying phenomenon from gestation to development and regression, and lack of intuitiveness and evidence. Therefore, it is still an unsolved problem to establish an appropriate mathematical model for panic group buying behavior. The effective use of mathematical models can make the complex and changeable network public opinion problems more intuitive and clear. Based on this, by combining public opinion transmission mechanism of panic, this paper tries to analyze mass buying behavior caused by panic, and makes improvement based on the traditional Susceptible-Infected-Recovered model (SIR). 9 Also, this paper blends the particularity of panic buying public opinion to establish a new transmission model, and analyzes the goods panic buying caused by the spread of panic in the uncertain environment with the calculation of simulation experiment. The structure of this paper is as follows: Literature Review is literature review, pointing out the innovation of this paper; Model Construction constructs the online transmission model of panic buying public opinion and further proposes the improved SIR model for public opinion transmission and the offline interaction behaviors to build a two-layer synchronous network; Simulation Experience uses computer simulation methods to analyze the factors affecting the public opinion transmission of material buying; the section headed Examples gives some cases to support the model; and, finally, Conclusion and Future Work gives the summary of the whole paper and the future research direction. At present, there is very limited research on panic buying. Some typical literatures are as follows: Yuen et al 8 provided a comprehensive explanation for the reasons of panic buying by integrating limited and scattered literatures, and pointed out that there was a lack of researches on panic buying at present. Rosnow and Rosenthal 10 proposed that emergencies would seriously threaten the basic operation of the social system, so both the government and citizens had to respond in the case of strong timeliness and uncertainty. Zheng consequences of disruption often stockpiled large amounts of products to mitigate the risk of future shortages. The shortage analysis was carried out from the perspective of supply chain. Kang et al 11 suggested that the media's depiction of people's panic buying might lead to people's distrust of society and subsequently lead to more panic buying. Arafat et al 12 put forward that the media play a significant role in dealing with panic buying as some of the psychological explanations are related to the perception of the general population. Therefore, in prevention, the media could play a vital role. Panic buying is a kind of impulsive consumption behavior. At present, there have been a lot of studies on it. For example, Shiv and Fedorikhin 13 proposed that impulsive consumption behavior was the result of the game between people's desire to buy and their self-control. Sarah and Arthur 14 proposed that negative emotions were an important incentive for impulsive consumption, and individuals often adjusted and alleviated their negative emotions through impulsive or even retaliatory shopping. Bosch and Stern 7 believed that impulse buying is influenced by a series of economic, time, place, and even cultural factors, and consumers who implemented impulse buying were often affected by strong and even irresistible impulse buying motivation. Weinberg 15 believed that impulsive consumption was an irrational behavior in which consumers had a strong emotional response to the stimulus in the shopping situation, and then took an immediate response without thinking. In the event of an emergency, it is often difficult for people to have enough time to make a reasonable decision, and individuals tend to be influenced by emotionality and conformity in their behavior. Therefore, due to the irrational characteristics of impulsive consumption and material panic buying behavior, many scholars try to study panic buying behavior from the emotional perspective. For example, Sharma and Alter 16 proposed that individuals often bought scarce commodities not because of the value contained in them, but of emotional psychological needs. Aguirre 17 believed that panic is a social behavior during a crisis, which was irrational, anti-social, and selfish, and pointed out that panic was hard to control and seriously affects consumers' behavior. Kemp et al 18 proposed that uncertainty would cause people's fear, and the emergence of fear psychology would lead to purchasing behavior. Anand 19 pointed out that risk perception referred to the individual's perception and cognition of external objective risks, emphasizing the influence of individual experience gained through subjective judgment on individual perception, which was used to express the attitude and intuitive judgment of objective risks, including the assessment and response to risks. Sheu and Kuo 20 proposed that hoarding prior to or during a disaster could be interpreted as a form of self-protection behavior. Many scholars believe that panic emotions can be mutually infected. For example, Hatfield et al 21 proposed that emotional contagion was the result of an individual's unified emotion by learning or imitating different information such as other individuals' language. Du et al 22 believed that emotional infection was a process of unconscious expression and transmission of similar emotions. Hoffman 23 believed that emotional infection was the emotional cognition and control formed by conscious performance. Therefore, conformity is also a factor that induces purchasing behavior. Deutsch and Gerard 24 divided conformity into informational conformity and normative conformity. The former referred to conformity conducted by an individual following the opinions of others, while the latter referred to the phenomenon that an individual keeps his own behavior consistent with others' in order to meet the expectations and norms of others or groups. In addition, some scholars have studied the purchasing behavior caused by disasters in real life. For example, George 25 studied the panic buying caused by the September 11 attack in the United States, and proposed that the panic buying in this event was the consumption behavior taken by people to protect themselves against terrorism. Soon 26 explored the cognition and trust of Malaysian consumers on food safety news conveyed by social media through a questionnaire, and proposed that fake food safety news released by social media might cause mass panic. Forbes 6 took the Christchurch earthquake in 2011 as an example to make an in-depth study of the changes in consumer preferences after disasters. Sawada et al 27 DovePress the nature, extent, and impact of panic buying on traditional media reports. They also pointed out that the media should be more cautious and responsible when reporting panic buying behavior. Minihan et al 30 conducted a study on the psychological problems caused by the global epidemic of COVID-19, suggesting that certain groups, such as people with mental illness, were particularly vulnerable during the pandemic and that governments need to provide maximum support to this group and their families. Arafat et al 4 pointed out that, since ancient times, panic buying behavior had been observed in public health emergencies. However, there was no exact psychological explanation for this. So, taking the COVID-19 pandemic as the entry point, they explored the psychological factors behind panic buying behavior of the masses during the epidemic, and proposed that fear of material scarcity and insecurity were the core factors that cause panic buying. Hobbs 31 discussed the impact of panic buying and hoarding behaviors on social stability in Canada during the COVID-19 pandemic from the perspective of supply chain resilience. Kerr 32 pointed out that the COVID-19 pandemic put unprecedented pressure on Canadian food supply chains. The first need was to focus on maintaining and enhancing supply chain resilience. From the above analysis, it can be seen there are only a few studies on panic buying at present, and most scholars try to explain panic buying from the perspective of impulsive consumption, but there is a lack of heterogeneous studies of the panic state. In fact, panic buying is fundamentally different from other buying behaviors. However, the research on the spread of panic emotion only exists in the perspective of theoretical analysis, and empirical research is lacking. There are few studies on the use of panic transmission theory to describe impulsive consumption. Regarding the particularity of panic buying public opinion, this paper tries to analyze the panic buying behavior caused by panic in combination with the communication mechanism of public opinion. Firstly, the traditional model of infectious disease spread was improved, and the susceptible person (S), infected person (I), and Immune person (R) in the SIR model. So, the model conforms to the rules of public opinion transmission in panic buying. Secondly, by integrating the particularity of panic buying public opinion, we established a new model of panic buying public opinion transmission. Finally, the problem of material buying caused by panic emotion under uncertain environment is analyzed through computational simulation experiment. This paper quantifies the panic buying behavior through the "Agent Based Model" (ABM) method. 33 The ABM method is a microscopic model, which can reproduce and predict complex phenomena by simulating the simultaneous actions and interactions of the multiple agents. This process is the emergence from the low micro to the high macro. In the paper, the individual who participates in the spread of panic on the network is represented as an agent. Set the network scale to be N, that is, there are N netizen nodes in the network. The individual attitude value in the network is represented by any number in the continuous interval [−1,1], and its initial attitude value follows the random value of uniform distribution. As shown in Figure 1 , each node in the network represents the individual participating in the public opinion transmission, and is represented as an agent in the model. In each interaction, the two individuals with connection relationship have a certain probability to interact with each other. Specific research methods are shown in Figure 2 . In this paper, we construct the panic transmission model and the occurrence judgment model to determine whether the individual will choose to participate in the rush purchase. At the same time, through the construction of an online and offline double-layer synchronous coupling network, we compare the impact of virtual network and offline life on individuals in the rush purchase event. Panic buying behavior refers to a kind of anti-risk behavior of some fearful individuals in society who feel threatened in the uncertain environment and perceive a scarcity of goods and materials. After an emergency occurs, individuals will form a series of opinions on the event after perceiving the information, and reach a certain consensus after mutual discussion, which is called panic buying public opinion. Then, panic buying public opinion urges individuals to take actions. As a result, offline panic buying events occur. The most critical factor in the formation of panic buying public opinion is the individual's judgment of the possibility of shortage at the next moment. However, due to the butterfly effect, 34 buying rice in the public opinion include: China's grain self-sufficiency rate reaches more than 80%; Vietnam and other major grain exporters restrict the export of grain; the face mask shortage event in early 2020 and the opinions of the friends around. Everyone's view of panic buying is a combination of all panic buying and opposing views. Banisch and Olbrich 35 put forward that the transformation of individuals from belief to attitude was realized through a coded cognitive evaluation graph. The facts of different dimensions affect their separate attitude judgment in their own dimensions. In addition, they proposed a multi-dimensional belief evaluation model to study multi-dimensional attitude transformation. Based on this model, this paper proposes a multi-dimensional opinion formation model suitable for panic buying public opinion. As shown in Figure 3 , the model assumes that every individual's opinion of the possibility of shortage at the next moment is composed of n arguments. It assumes that some of these arguments support panic buying while some do not. For example, the argument that "China's grain selfsufficiency rate reaches more than 80%" would not support panic buying, while the argument that "Vietnam and other major grain exporting countries restrict the export of grain" would support panic buying. However, these arguments are independent of each other, namely, China's grain self-sufficiency rate of 80% is not correlated with Vietnam's export restriction. Therefore, assuming that the decision space "Ω" of agent i composed of n independent elements, shown as: Therefore, it is assumed that agent i can judge the probability of shortage based on the information found and define it as R i . Its value can be calculated as follows: From the above analysis, we can know that R i 2 0; 1 ½ �. In addition, when individuals interact with each other on panic buying public opinions, they will believe that the information delivered by those who have similar opinions to themselves is more trustable. 36 Therefore, individual j's persuasion degree over i is positively correlated with the degree of similarity between the interlocutors. The degree of similarity between two individuals can be expressed by the repetition degree of each argument. If I jk =I ik ≠0, it means that both individual i and individual j have certain opinions on argument k, and their opinions are consistent, that is, they have similar opinions on argument k. Assuming the parameter κ ij is the number of I jk =I ik ≠0 (k=1~n) at time t, the conviction function R ij of individual i to j can be calculated as Note: As shown in Formula 5 and Figure 4 , at time t, the information of agent j is transmitted to agent i and has influence on agent i. Assuming that there is a threshold δ, if κ ij is greater than δ, agent i will believe that the opinion of agent j is trustable. Therefore, at the next moment, i will choose to modify his opinion by referring to agent j's opinion. Mass panic buying is a group behavior driven by panic emotion. Individuals in an uncertain environment will produce a series of psychological reactions, the most obvious of which is panic. 37 When individuals with low risk perception feel the risk in the network and start to worry about the lack of materials, they will also spread the panic while buying goods and materials, so as to spread the panic and trigger large-scale panic buying behavior. Based on this, panic can be contagious. When in a panic state, agent j will continue to spread panic emotion to the outside, leading to other individuals falling into panic. Suppose the panic coefficient of agent i at time t is expressed as E i (t) (E i (t)∈[0,1]). At this point, due to the panic emotion conveyed by agent j, the panic degree of agent i at the next moment changes. In addition, the level of individual panic is also affected by uncertainty. After the occurrence of public health and other emergencies, if the government fails to convey effective information in a timely manner or the government information is not transparent enough, individual panic will rise. Therefore, the degree of panic is also related to uncertainty, which is specifically calculated as follows: where parameter C represents the conformity of the individual, representing the degree of individual i is affected by the panic emotion of others; the parameter ε represents the degree of uncertainty perceived by the individual. The panic buying behavior of individuals in the panic state is jointly influenced by the shortage probability R i (t) and the panic coefficient E i (t) judged by the individuals themselves. Artificial potential field theory 38 holds that individual behaviors of social networks can be described by constructing artificial potential field functions, which are specifically represented by searchers finding and reaching target information points according to potential difference. When agent j passes an opinion to agent i, agent j transmits his judgment about the probability of a shortage, the R j , and the panic coefficient, the E j . Specifically, agent j exerts gravity on agent i, through the artificial potential field, and agent i is affected and changes accordingly. Figure 5 shows the artificial potential field of panic buying public opinion, and the shaded part is the buying interval. When the agent i opinion x i (E i , R i ) is within the buying interval, it means that i participates in the buying behavior. According to mathematical information, node x i (E i , R i ) is located in the non-panic buying interval of the figure under the constraint conditions as follows: subject to EiðtÞ<À e r RiðtÞ þ e 0 � EiðtÞε, it is likely to be out of stock in the network. Suppose that if E i t ð Þ The supplementary Formulas 13-14 are retested, and the results are shown in Figure 16 : As Figure 16 shows, in the case of insufficient supplies, panic in a few areas can have an impact on the whole society. Therefore, it is necessary to ensure adequate supplies when rumors of shortages spread widely on the Internet. We studied the corresponding news and found that the real phenomena match our simulation results. In our simulation, we derived the outcomes that individuals' face-toface interaction promotes the transmission of panic in a certain degree. For instance, initially, the COVID-19 pandemic took off in Washington State and California in the USA in early March 2020. The two states had about 1000 confirmed cases and 32 deaths of the disease. Because of few confirmed cases and deaths in other DovePress areas in the USA, the panic in other states was not much at that time. Meanwhile, the USA did not carry out shutdown or enforce the order of mandatorily wearing facemasks. However, people's face-to-face interaction came alive over the two states, and resulted in panic shopping. People were talking with each other and were stocking up on soap, hand sanitizers, and alcohol wipes because they believed that the stuff can protect them from being infected with the virus. The offline interaction pushed the spread of panic, and then many people began to post their concerns of the virus and their actions of panic buying on local social networking platforms, such as Nextdoor, which is a neighborhood hub for trusted connections and the exchange of information in the local region. Soon after, many people posted their opinion on Facebook and Twitter, making the panic spread to all over the USA, even to the areas that had very few confirmed cases and deaths. As it turned out, the offline interaction promoted the transmission of panic. In our simulation, we also found that the large-scale outbreak of panic in an area will indirectly affect the public opinion of the entire society, and then result in large-scale panic buying. For example, the mid-center China reported the outbreak of the COVID-19 pandemic at first. Then panic buying emerged at some cities in China mainland. Soon after, rumors started that Hong Kong will be short of groceries such as toilet paper because it imports most of the goods from China mainland. Therefore, some people in Hong Kong began their crazy buying for various goods, especially for toilet paper and sanitizers. At the same time, social media in Japan erroneously posted that toilet paper and groceries were running low, as China would no longer supply these products, leading to panic buying in Japan as well. In Singapore and Australia, the toilet paper started disappearing in supermarkets and panic buying even led to people fighting in the checkout queue. These examples illustrate that the panic affects the public opinion over the whole society, and in turn incurs largescale panic buying. Moreover, we found that when numerous instances of panic buying take place in a local area, it is necessary to ensure plenty of supplies in other regions, otherwise it will cause the entire society to fall into panic buying. For example, when panic buying occurred in a few states in the USA, such as Washington state and California, people in many other states were flocking to supermarkets as well. Shoppers not only stocked up sanitizers and toilet paper, but also swept food, bottled water, even TV sets and bicycles. People's behavior in this manner made shortages worse. The effective strategy is to offer sufficient materials in supermarkets. Costco, Walmart, and Kroger, the three large-scale retailers with many supermarkets in the USA, dispatched goods from their warehouses or called in supplies from the manufacturers in a short time. On the other side, the US government gave the retailers many preferential policies in terms of taxation, circulation, and commerce to help them provide enough goods to consumers. Consequently, the panic buying was calmed down and people's lives returned to regular order. Figure 17 is the simulation of public opinion on panic goods rush purchase in this paper. It can be seen from Figure 17 that in the initial stage of public opinion transmission, ensuring sufficient supply of materials can reduce the number of individuals participating in the rush buying. However, if comparing with Figure 17(A-D) , it is not difficult to see that their convergence is similar when time=5000 after material shortage. This shows that before the end of the panic buying public opinion, if there is a lack of material supply, the number of rush buyers will rise rapidly. This explains the reason why it is difficult to stop the buying events in many areas during the COVID-19 pandemic. Therefore, in the event of major public health events, ensuring adequate supply of materials is the top priority. In a specific environment, the spread of rumors of shortage will easily lead to large-scale panic buying. In this paper, the online and offline two-layer synchronous coupling network is constructed, and on this basis the material panic model is constructed to analyze the influencing factors of panic public opinion transmission. The main work of this paper is as follows: (1) Based on the classic BA network, the online directed public opinion transmission-network is constructed, and the offline transmission-network is integrated to construct the (2) The individual decision model and the panic transmission model under the uncertain environment are constructed. (3) Simulation results show that: 1) The offline interaction of individuals promotes the transmission of panic to a certain extent; 2) When there is large-scale panic buying in local areas, it is necessary to ensure sufficient supplies in other areas, otherwise the whole society will fall into a state of panic buying. (1) This paper proposes that group panic is related to uncertainty. However, the deep reasons for the transmission of group panic caused by uncertainty remain to be further studied. At present, the research on behavior choice of micro individuals in uncertain environment represented by DS evidence theory (proposed by Dempster and Shafer) 42 has achieved preliminary results, and its combination with network public opinion is of great research value. (2) In fact, the transformation of panic emotion generated by micro individuals and macro public opinion is a very complex process, so it is necessary to explore the relationship between them in future research. Using psychological theory to explain this may be a possible method. (3) There is a virtual feature in the network, which makes it difficult for individuals to distinguish the induced stock out rumor information. With the constant disclosure of the truth, the phenomenon of public opinion reversal appears from time to time. In the study of panic buying, there are still few literatures on public opinion reversal. But the phenomenon of public opinion reversal is a very important and urgent problem to be solved. (4) The artificial intelligence algorithm based on big data greatly strengthens the attributes of human social relations. The rise of social computing, which takes communication network analysis, communication text mining, and so on as the main analysis tools, has provided some assistance for the computability foundation of human communication behavior. Therefore, through the analysis of human communication behavior data contained in the Internet big data, we can deeply mine the patterns and rules behind human communication behaviors, and analyze the generation mechanism and basic principles behind the patterns. The datasets generated and analyzed during the current study are available from the corresponding author. 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