A study on the perception of authenticity of tourist destinations and the place attachment of potential tourists-the case of Ding Zhen's endorsement of Ganzi, Sichuan

Despite the COVID-19 pandemic, which has lasted for more than two years and significantly affected tourism, people's yearning for better tourism has never weakened, and China's Dual Carbon Strategy further enhanced their desire. At the end of 2020, Ding Zhen shot to stardom on the Internet because of his rugged good looks, further improving the reputation of Ganzi, Sichuan Province, and demonstrating tourists' desire to appreciate authentic destinations. The following are worthy of research: the factors affecting tourists' perceptions of the authenticity of destinations, the relationship between perceptions of authenticity and place attachment in potential tourists, and methods to strengthen the authenticity of a destination to enhance the place attachment of potential tourists. Taking Ganzi and Ding Zhen as the research subjects, this paper uses a co-occurrence network map analyzing the four kinds of authenticity perceived for tourist destinations, based on user-generated content, and uses the term frequency-inverse document frequency method to further calculate the weight score of factors. Finally, the paper verifies the influence of each dimension on place attachment by calculating comment sentiment scores. The results show that (1) natural landscapes, human characteristics, and preferential measures significantly impacted the perception of authenticity among potential tourists;(2) among the four kinds of authenticity perceptions, humanistic authenticity was the major promoting factor by which potential tourists formed place attachment. Based on the research results, the paper puts forward a series of suggestions to improve tourists' emotional attachment to destinations, including creating cultural symbols of local differences, adopting preferential policies for local tickets in a timely manner, and emphasizing nature protection.

Analysis of the Public Opinion Evolution on the Normative Policies for the Live Streaming E-Commerce Industry Based on Online Comment Mining under COVID-19 Epidemic in China

Recent years have witnessed the intensive development of live streaming E-commerce, an emerging business mode. Although it contributes to economic growth, various forms of chaos show up and disturbs the market order. Therefore, from 1 July 2020, the official release of the first domestic document on live streaming E-commerce, the Code of Conduct for Online Live Streaming Marketing, to the end of the first half of 2021, China has witnessed so intensive release of relevant policies that are rare over the past years. Introducing these policies will inevitably attract the general public's attention and discussions. Based on online comments, this paper uses the LDA models to extract topics from online comments related to live streaming E-commerce and identifies sentiment polarity and sentiment intensity by the analysis models of different emotion dictionaries to study policy implementation effects and the main topics of concern before and after the policy implementation. The analysis results show that people between the age of 20 and 40 attach more importance to the implementation of the normative policy for live streaming E-commerce. Women, the main force of live streaming users, are less enthusiastic about the policy implementation than men. Moreover, the analysis results of the LDA models and online HDP (online hierarchical Dirichlet process) models demonstrate that the most discussed topics are the contribution of live streaming E-commerce to traditional economic transformation, public welfare activities, resumption of work and production, and poverty alleviation, as well as fraud, counterfeit goods, supervision, rights protection and other incidents in this industry. Overall, the majority of the public holds a positive attitude towards the policy implementation. After further analysis of comments under the relevant topics, it is found that compared with the first two policies released on 1 July and 5 November in 2020, although the proportion of netizens with positive emotions during the implementation of the follow-up policy has increased, the increment is not significant, indicating that the implementation of the new normative policy in a short term will hardly curb the occurrence of industry chaos. In turn, the governments should transfer their attention to actual regulatory problems, and intensify efforts to implement normative policies. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Identifying a New Social Intervention Model of Panic Buying Under Sudden Epidemic.

COVID-19 that broke out at the end of 2019 continues to spread globally, with frequent occurrence of variant disease strains, thus epidemic prevention and control become a kind of routine job. At present, due to the prevention and control measures such as maintaining social distance and community blockades, there is a boom in material purchases in many places, which not only seriously endangers social order and public environmental safety, but also easily leads to the interruption of the supply chain and the shortage of social materials. This article aims to study the intervention methods to curb the spread and spread of panic buying behavior. Firstly, through crawler technology and LDA (Latent Dirichlet Allocation) topic model, this article analyzes the intervention measures taken by various social forces in China to curb the spread of panic buying, and summarizes the multi-channel intervention measures including online and offline forms. Secondly, through the multi-Agent Monte Carlo method, the targeted intervention mechanism is supplemented in each propagation link of the panic buying propagation model, and a new social intervention model of panic buying under sudden epidemic is constructed. Then, through MATLAB modeling and simulation, the main factors affecting panic buying intervention are discussed. The simulation results show that: (1) The single plan with the best intervention effect is the supply monitoring. While the official response can play an immediate inhibitory effect, but it is affected by credibility and timeliness. The intervention effect of psychological counseling is limited, and it generally needs to be used in combination with other measures. (2) The combination strategy with the best intervention effect is "supply monitoring + official response + psychological counseling," and the worst is "information review and guidance + psychological counseling." Supply monitoring is a key measure to curb panic buying. At the same time, "information review and guidance" will have a certain counter-effect in the combined strategy. Finally, the effectiveness and universality of the proposed model are verified by examples of China and Britain.

Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

At present, rumors appear frequently in social platforms. The rumor diffusion will cause a great impact on the network order and the stability of the society. So it's necessary to study the diffusion process and develop the rumor control strategies. This article integrates three heterogeneous factors into the SEIR model and designs an individual state transition mode at first. Secondly, based on the influencing factors such as the trust degree among individuals, an individual information interaction mode is constructed. Finally, an improved SEIR model named SEIR-OM model is established, and the diffusion process of rumors are simulated and analyzed. The results show that: (1) when the average value of the interest correlation is greater, the information content deviation is lower, but the rumor diffusion range will be wider. (2) The increase of the average network degree intensifies influence of rumors, but its impact on the diffusion has a peak. (3) Adopting strategies in advance can effectively reduce the influence of rumors. In addition, the government should enforce rumor-refuting strategies right after the event. Also, the number of rumor-refuting individuals must be paid attention to. Finally, the article verifies the rationality and effectiveness of the SEIR-OM model through the real case.

Based on Computational Communication Paradigm: Simulation of Public Opinion Communication Process of Panic Buying During the COVID-19 Pandemic.

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.

Modeling Multi-Dimensional Public Opinion Process Based on Complex Network Dynamics Model in the Context of Derived Topics

With the rapid development of the Internet, the speed with which information can be updated and propagated has accelerated, resulting in wide variations in public opinion. Usually, after the occurrence of some newsworthy event, discussion topics are generated in networks that influence the formation of initial public opinion. After a period of propagation, some of these topics are further derived into new subtopics, which intertwine with the initial public opinion to form a multidimensional public opinion. This paper is concerned with the formation process of multi-dimensional public opinion in the context of derived topics. Firstly, the initial public opinion variation mechanism is introduced to reveal the formation process of derived subtopics, then Brownian motion is used to determine the subtopic propagation parameters and their propagation is studied based on complex network dynamics according to the principle of evolution. The formula of basic reproductive number is introduced to determine whether derived subtopics can form derived public opinion, thereby revealing the whole process of multi-dimensional public opinion formation. Secondly, through simulation experiments, the influences of various factors, such as the degree of information alienation, environmental forces, topic correlation coefficients, the amount of information contained in subtopics, and network topology on the formation of multi-dimensional public opinion are studied. The simulation results show that: (1) Environmental forces and the amount of information contained in subtopics are key factors affecting the formation of multi-dimensional public opinion. Among them, environmental forces have a greater impact on the number of subtopics, and the amount of information contained in subtopics determines whether the subtopic can be the key factor that forms the derived public opinion. (2) Only when the degree of information alienation reaches a certain level, will derived subtopics emerge. At the same time, the degree of information alienation has a greater impact on the number of derived subtopics, but it has a small impact on the dimensions of the final public opinion. (3) The network topology does not have much impact on the number of derived subtopics but has a greater impact on the number of individuals participating in the discussion of subtopics. The multidimensional public opinion dimension formed by the network topology with a high aggregation coefficient and small average path length is higher. Finally, a practical case verifies the rationality and effectiveness of the model proposed in this paper.

Evolutionary Game of Multi-Subjects in Live Streaming and Governance Strategies Based on Social Preference Theory during the COVID-19 Pandemic

After the outbreak of the COVID-19, offline consumption has been significantly impacted. For the sake of safety, online consumption has become the most common manner, and this has generated e-commerce, which not only breaks the spatio-temporal or regional restrictions, but also conforms to the normal economic development needs for epidemic prevention and control. However, this new business model causes problems such as the shortage of post-sales service, false publicity, and uneven quality of live streaming anchors, seriously affecting the interests of consumers. Therefore, it is urgent to strengthen the management of the chaos of live streaming. This study focuses on exploring the problems and the behavioral strategies of stakeholders in the governance process. The paper obtained online user comments by python, and used topic clustering and subject extraction methods to dig out the problems and related multiple subjects in live streaming at first. Secondly, the theory of social preference was introduced to construct an evolutionary game model among multiple subjects, and how to guide the behavioral decision-making of multiple subjects to standardize and rationalize was studied, so as to control the problem of live streaming. Finally, simulation experiments were conducted and the results demonstrated that: (1) Compared with strengthening the reciprocal preference of the government, live streaming platforms, and consumers, changing the individual’s altruistic preference is more effective in controlling the chaos of live streaming;(2) weakening the platform’s altruistic preference for anchors is conducive to creating a good live streaming environment;and (3) changing consumers’ altruistic preference or reciprocal preference is less effective in promoting the governance of the live streaming environment.

Analysis of Social Effects on Employment Promotion Policies for College Graduates Based on Data Mining for Online Use Review in China during the COVID-19 Pandemic.

As an important part of human resources, college graduates are the most vigorous, energetic, and creative group in society. The employment of college graduates is not only related to the vital interests of graduates themselves and the general public, but also related to the sustainable and healthy development of higher education and the country's prosperity through science and education. However, the outbreak of COVID-19 at the end of 2019 has left China's domestic labor and employment market in severe condition, which has a significant impact on the employment of college graduates. Based on the situation, the Chinese government has formulated a series of employment promotion policies for college graduates in accordance with local conditions to solve the current difficulties in employment of college graduates during the COVID-19Pandemic. Do these policies meet the expectations of the people? Is the policy implementation process reasonable? All these issues need to be tested and clarified urgently. This paper takes the employment promotion policy of college graduates under the COVID-19 as the research object, uses the PMC index model to screen the policy texts, obtains two perfect policy texts, and uses the Weibo comments to construct the evaluation model of policy measures support degree to analyze the social effects of employment promotion policies for college graduates. The results show that the public's support degree with the employment promotion policies for college graduates under COVID-19 needs to be improved. Among them, the public has a neutral attitude towards position measures and transference measures but is obviously dissatisfied with subsidy measures and channel measures. Finally, suggestions for improving policy are given to make the employment policy in line with public opinion and effectively relieve the job hunting pressure of college graduates.

The impact of the COVID-19 pandemic on user experience with online education platforms in China

During the COVID-19 pandemic, social education has shifted from face to face to online in order to avoid large gatherings and crowds for blocking the transmission of the virus. To analyze the impact of virus on user experience and deeply retrieve users' requirements, this paper constructs a reasonable evaluation index system through obtaining user reviews about seven major online education platforms before and after the outbreak of COVID-19, and by combining the emotional analysis, hot mining technology, as well as relevant literature. At the same time, the variation coefficient method is chosen to weigh each index based on the difference of index values. Furthermore, this paper adopts the comprehensive evaluation method to analyze user experience before and after the outbreak of COVID-19, and finally finds out the change of users' concerns regarding the online education platform. In terms of access speed, reliability, timely transmission technology of video information, course management, communication and interaction, and learning and technical support, this paper explores the supporting abilities and response levels of online education platforms during COVID-19, and puts forward corresponding measures to improve how these platforms function.

Propagation Model of Panic Buying Under the Sudden Epidemic.

The sudden outbreak of COVID-19 at the end of 2019 has had a huge impact on people's lives all over the world, and the overwhelmingly negative information about the epidemic has made people panic for the future. This kind of panic spreads and develops through online social networks, and further spreads to the offline environment, which triggers panic buying behavior and has a serious impact on social stability. In order to quantitatively study this behavior, a two-layer propagation model of panic buying behavior under the sudden epidemic is constructed. The model first analyzes the formation process of individual panic from a micro perspective, and then combines the Susceptible-Infected-Recovered (SIR) Model to simulate the spread of group behavior. Then, through simulation experiments, the main factors affecting the spread of panic buying behavior are discussed. The experimental results show that: (1) the dissipating speed of individual panics is related to the number of interactions and there is a threshold. When the number of individuals involved in interacting is equal to this threshold, the panic of the group dissipates the fastest, while the dissipation speed is slower when it is far from the threshold; (2) The reasonable external information release time will affect the occurrence of the second panic buying, meaning providing information about the availability of supplies when an escalation of epidemic is announced will help prevent a second panic buying. In addition, when the first panic buying is about to end, if the scale of the second panic buying is to be suppressed, it is better to release positive information after the end of the first panic buying, rather than ahead of the end; and (3) Higher conformity among people escalates panic, resulting in panic buying. Finally, two cases are used to verify the effectiveness and feasibility of the proposed model.

Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity.

The wide dissemination of false information and the frequent occurrence of extreme speeches on online social platforms have become increasingly prominent, which impact on the harmony and stability of society. In order to solve the problems in the dissemination and polarization of public opinion over online social platforms, it is necessary to conduct in-depth research on the formation mechanism of the dissemination and polarization of public opinion. This article appends individual communicating willingness and forgetting effects to the Susceptible-Exposed-Infected-Recovered (SEIR) model to describe individual state transitions; secondly, it introduces three heterogeneous factors describing the characteristics of individual differences in the Jager-Amblard (J-A) model, namely: Individual conformity, individual conservative degree, and inter-individual relationship strength in order to reflect the different roles of individual heterogeneity in the opinions interaction; thirdly, it integrates the improved SEIR model and J-A model to construct the SEIR-JA model to study the formation mechanism of public opinion dissemination and polarization. Transmission parameters and polarization parameters are simulated and analyzed. Finally, a public opinion event from the pricing of China's self-developed COVID-19 vaccine are used, and related Weibo comment data about this event are also collected so as to verify the rationality and effectiveness of the proposed model.

Modeling, simulation, and case analysis of COVID-19 over network public opinion formation with individual internal factors and external information characteristics.

With the development of information technology, the Internet has become an important channel of public opinion for expressing public interests, emotion, and ideas. Public emergency usually spreads via network. Due to the temporal and spatial flexibility and the information amplification of network, the opinions from different regions and background are easy to be represented as network public opinion, and have important impact on social and economic life. Thus, studying the formation mechanism of network public opinion has important theoretical and practical significance. Taking the formation process of network public opinion under emergencies as the research object, this paper first identifies the key factors influencing the formation of network public opinion, namely the internal characteristics (include individual education level, individual stubbornness, individual initial opinion, and so on) and external information of individuals (include external information intensity). Second, information intensity is introduced to describe the influence of external information feature on the formation of network public opinion. Individual education level, individual stubbornness, and individual initial opinion are analyzed to describe the influence of individual internal factors on the formation, and then its model is constructed. Through the simulation experiments, this paper analyzes the influence of external information intensity, individual education level, individual stubbornness, individual initial opinion, and other factors on the formation of network public opinion. The simulation results show that: (1) the greater intensity of public emergency reporting causes the easier formation of network public opinion; (2) the higher individual education level leads to the shorter time for completing the final formation and stable state of online public opinions, and after the formation of online public opinions, the opinion of the event is mainly neutral; (3) the greater individual's stubbornness makes the shorter formation time of online public opinion. When online public opinion reaches a stable state, the neutral opinion group dominates and firmly controls the development trend of public opinion; (4) the difference of opinions among individuals is the most important factor affecting the formation of network public opinion. Finally, the rationality and validity of the proposed model are verified by a real case. Compared with previous studies on the formation mechanism of network public opinion, this paper divides the formation process of network public opinion into three stages: individual information perception, individual decision making, and individual opinion transmission. Meanwhile, the influence of individual internal factors and external information characteristics on the formation process of network public opinion is also considered.

A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression.

BACKGROUND AND AIM: At the end of 2019, the outbreak of COVID-19 had a significant impact on China's tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas. METHODS: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users' perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotion policies and promote the rapid revitalization of the cultural and tourism industry. RESULTS: The results show that from the single policy, the social effect of the "safety" policy is the best. From the perspective of combination policies, the simultaneous release of "safety" policies and "economy" policies have the greatest social impact, which can dramatically accelerate the recovery of the cultural and tourism industry. Finally, this paper proposes suggestions for policy formulation to improve the ability of the cultural tourism industry to cope with crisis events. CONCLUSION: These results explain the perceived effects of the public on the government policies and can be used to judge whether the policies have been released in place. Based on the above results, corresponding suggestions are proposed as follows: 1) the combination of economic policies and security policies can achieve better results; and 2) the role of "opinion leaders" can be played to improve the perceived effect of policies.

Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic.

The outbreak of Corona Virus Disease 2019 (COVID-19) in various countries at the end of last year has transferred traditional face-to-face teaching to online education platforms, which directly affects the quality of education. Taking user satisfaction on online education platforms in China as the research object, this paper uses a questionnaire survey and web crawler to collect experience data of online and offline users, constructs a customer satisfaction index system by analyzing emotion and the existing literature for quantitative analysis, and builds aback propagation (BP) neural network model to forecast user satisfaction. The conclusion shows that users' personal factors have no direct influence on user satisfaction, while platform availability has the greatest influence on user satisfaction. Finally, suggestions on improving the online education platform are given to escalate the level of online education during the COVID-19 pandemic, so as to promote the reform of information-based education.