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Background: University emergencies, garnering significant public attention and shaping network opinions, pose a crucial challenge to universities' management and societal stability. Hence, network public opinion on university emergencies is a vital issue. Nevertheless, the underlying mechanism has not been fully explored and cannot be efficiently controlled. This study aimed to explore the formation pattern of network public opinion on university emergencies, analyze its causes, and provide scientific governance strategies for coping with this issue. Methods: Based on a sample set of 204 cases from the Zhiwei Data Sharing Platform, this study classifies network public opinion on university emergencies into six types and visually analyzes their characteristics: time distribution, subject, duration, and emotion. By integrating the theory of the network public opinion field, this study develops a network public opinion field model of university emergencies to reveal its formation pattern. Furthermore, it analyzes the causes of network public opinion on university emergencies from the perspective of the public opinion lifecycle and proposes corresponding governance strategies. Results: The sample consisted of 304 cases of real-life public opinion, and the visualization results show that public opinion on mental health and teacher-student safety constitutes the predominant types, accounting for 83.3%. High-occurrence subjects are public universities (88.24%) and students (48%). The most frequent months are July and December. 90.20% of the public opinions have a lifespan of less than 19 days, with an impact index ranging from 40 to 80. The public's emotional response to different types of public opinion varies, with negative emotions dominating. Conclusion: This study provides novel insights for understanding their formation and dissemination. It also provides practical implications for relevant departments to govern network public opinion on university emergencies.

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Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an opinion dynamics model, which would provide a theoretical method for public opinion management. Based on the classical bounded confidence model, we extract the information quality variables and individual trust threshold and introduce them to construct our two-stage opinion evolution model. And then in the simulation experiments, we analyze the different effects of opinion information quality, opinion release time, and frequency on public opinion by adjusting the different parameters. Finally, we added a case to compare real data, the data from classical model simulation and the data from improved model simulation to verify the effectiveness on our model. The research found that the more sufficient the argument and the more moderate the attitude, the more likely to guide the public opinion. If public person holds different opinions and different information quality, he should choose different time to present his opinion to achieve ideal guide effect. When public person holds neutral opinion and the information quality is relatively general, he/she can intervene in public opinion as soon as possible to control final public opinion; when public person holds extreme opinion and the information quality is relatively high, he/she can choose to express opinion after a certain period on public opinion evolution, which is conducive to improve the guidance effect on public opinion. The frequency of releasing opinions of public person consistently has a positive impact on the final public opinion.

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Identifying and analyzing the public's opinion of focal events during a major epidemic can help the government grasp the vicissitudes of network public opinion in a timely manner and provide the appropriate responses. Taking the COVID-19 epidemic as an example, this study begins by using Python-selenium to capture the original text and comment data related to COVID-19 from Sina Microblog's CCTV News from Jan. 19, 2020, to Feb. 20, 2020. The study subsequently uses a manual interpretation method to classify the Weibo content and analyzes the shifting focus phenomena of network public opinion based on the moving average method. Next, the study uses an enhances TF-IDF to extract keywords from the Weibo comment and uses the keywords to construct a word co-occurrence network. The results show that during the epidemic, the network public opinion focus shifted significantly over time. With the progression of the epidemic, the focus of network public opinion diversified, and various categories stabilized. Compared to simple keyword and text classification recognition focus problems, the proposed model, which is highly accurate, identified multiple network public opinion focus problems and described the core contradictions of the different focus problems.

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The aim is to clarify the evolution mechanism of Network Public Opinion (NPO) in public emergencies. This work makes up for the insufficient semantic understanding in NPO-oriented emotion analysis and tries to maintain social harmony and stability. The combination of the Edge Computing (EC) and Deep Learning (DL) model is applied to the NPO-oriented Emotion Recognition Model (ERM). Firstly, the NPO on public emergencies is introduced. Secondly, three types of NPO emergencies are selected as research cases. An emotional rule system is established based on the One-Class Classification (OCC) model as emotional standards. The word embedding representation method represents the preprocessed Weibo text data. Convolutional Neural Network (CNN) is used as the classifier. The NPO-oriented ERM is implemented on CNN and verified through comparative experiments after the CNN's hyperparameters are adjusted. The research results show that the text annotation of the NPO based on OCC emotion rules can obtain better recognition performance. Additionally, the recognition effect of the improved CNN is significantly higher than the Support Vector Machine (SVM) in traditional Machine Learning (ML). This work realizes the technological innovation of automatic emotion recognition of NPO groups and provides a basis for the relevant government agencies to handle the NPO in public emergencies scientifically.

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Food additives play an important role in the food supply, and it has been a food safety topic of great concern to the public. There has been no systematic research on Chinese consumers' concerns, attitudes, feelings, or opinions on supervision and media coverage of food additives in the past decade, which is an area worth exploring. This study was carried out to deeply understand consumers' cognition of food additives and formulate food safety risk communication strategies of food additives in China. Big data of consumers' online public opinion of China on food additives from 2011 to 2020 was collected and cleaned up using Haina Network Public Opinion Monitoring System version 2.0 (HNPOMS V2.0), followed by data analysis and visual display with the Ansi Food Safety Risk Communication System version 2.0 (AFSRCS V2.0). The results showed that the types of food additives of concern to the public have changed from 2011 to 2020, but the amount of food additives has always been of concern. The type of incident that the public is most concerned about is the illegal addition or abuse of additives. The public's confidence in food production enterprises has been insufficient, but the functions of market supervision are becoming clearer and clearer, and their expectations are constantly increasing. Consumers' cognition level increases with the strengthening of publicity and popular science, but the influence of "self-media" on public cognition is increasing day by day, and there is cognitive deviation, making it easy to mislead the public. Consumers' cognition of food additives is the basis of risk communication. Combined with the research results, this paper puts forward corresponding suggestions on the market and social supervision measures, network media guidance strategy and risk communication strategy of China, respectively.

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Analysis of network public opinion can help to effectively predict the public emotion and the multi-level government behaviors. Due to the massive and multidimensional characteristics of network public opinion data, the in-depth value mining of public opinion is one of the research bottlenecks. Based on Term Frequency-Inverse Document Frequency (TF-IDF) and deep learning technologies, this paper proposes an advanced TF-IDF mechanism, namely TF-IDF-COR, to extract text feature representations of public opinions and develops a CNN-based prediction model to predict the tendency of publics' emotion and mental health. The proposed method can accurately judge the emotional tendency of network users. The main contribution of this paper is as follows: (1) based on the advantages of TF-IDF mechanism, we propose a TF-IDF-COR mechanism, which integrates the correlation coefficient of word embeddings to TF-IDF. (2) To make the extracted feature semantic information more comprehensive, CNN and TF-IDF-COR are combined to form an effective COR-CNN model for emotion and mental health prediction. Finally, experiments on Sina-Weibo and Twitter opinion data sets show that the improved TF-IDF-COR and the COR-CNN model have better classification performance than traditional classification models. In the experiment, we compare the proposed COR-CNN with support vector machine, k-nearest neighbors, and convolutional neural network in terms of accuracy and F1 score. Experiment results show that COR-CNN performs much better than the three baseline models.

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With the increasingly complex social situation, the problems of traditional online public opinion governance are increasingly serious. Especially the problem of transmission efficiency, public opinion data management and user information security of Internet users is urgently needed. Here, we design a functional infrastructure framework of the network public opinion collaborative governance model based on the blockchain with strong practicality and comprehensiveness. In order to reach the consensus mechanism requirements under the framework, the algorithm is improved on the basis of the defects of the traditional DPoS consensus algorithm. Considering time dynamic factors in the process of reaching consensus, the paper proposes a reputation-based voting model. Furthermore, the paper purposes a rewards and punishments incentive mechanism, and also designs a new method of counting votes. From the simulation results, it was found that after the improvement of the algorithm, the enthusiasm of node participation was significantly increased, the proportion of error nodes was significantly reduced, and the operating efficiency was significantly improved. It shows that the improved consensus algorithm we propose applies to public opinion governance can not only improve the security of the system with the reduce of false public opinion spreading, but also improve the efficiency of information processing, so it can be well applied to information sharing and public opinion governance scenarios.

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Objective: To obtain the influencing factors of public opinion reactions and to construct a basic framework of the factors causing the occurrence of online public opinion in the epidemic area. Methods: The hot news comments on microblogs during the epidemic in Shanghai were collected and analyzed with qualitative analysis, grounded theory, and the "Wuli-Shili-Renli" (WSR) methodology as an auxiliary method. Results: (1) Three core categories of the Wuli system, the Shili system, and the Renli system, 15 main categories, and 86 categories that influence the development of network public opinion are obtained. (2) WSR Elements Framework Of Network Public Opinion (WSR-EFONPO) is established. (3) The WSR-EFONPO is explained. Conclusion: The framework of factors for the occurrence of network public opinion is proposed, and the development process of network public opinion under COVID-19 is sorted out, which is of great theoretical value in guiding the public in the epidemic area to form reasonable behavior.

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Research on social public opinion of new media is currently an important interdisciplinary topic in the international academic community. Under the background of COVID-19, the major public health event of in China, this research took social workers as the research object who worked during the period of epidemic prevention and control. It referred to the international research on public opinion and selected 63 related hotly discussed articles and public comments on the WeChat public platform, the new Chinese Internet media. Moreover, the research conducted text mining on related public opinion with the 5 W communication model from public opinion evolution, text content, communication media, audiences, and public opinion influence, and used grounded theory building a development model of the generation of network public opinion. It also put forward the development needs of social work in the aspects of community resilience, social work practice, lack of public health social workers, and big data warning, etc., and pointed out that social work lacks its proper structural status in China's public health system and emergency management system.

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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.

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【Objective】 To analyze the network public opinion on national blood safety in 2019, so as to effectively deal with and prevent the risk of it and create a favorable climate for voluntary blood donation. 【Methods】 Keywords such as blood donation policy, blood supply and blood transfusion risk were set up in TRS OM 5.0, and the network public opinion on national blood safety in 2019 was collected. The trend of network public opinion, regional distribution and key public opinion events were analyzed. 【Results】 A total of 2 299 581 pieces of public opinion information were collected in 2019. The trend was seasonal. The information was relatively intensive in February, April, June and December, during which hot events happened frequently. The information mainly concentrated in Beijing, Shanghai, Jiangsu, Shandong and Zhejiang. In terms of microblog netizens’ opinion. 57.1% tended to support, and 31.9% showed no support. The top 10 network public opinion events on national blood safety in 2019 were selected according to the heat value, among which 8 were positive and 2 were negative. 【Conclusion】 Network public opinion on blood safety in 2019 was mainly positive. The most intensive public opinion mainly concentrated in Beijing, Shanghai, Tianjin, Hainan, Qinghai, and Chongqing. However, as a province with a large number of ethnic minorities, Qinghai, ranked among the top 5 in China in terms of the network public opinion on blood safety per thousand, which deserved special attention and further analysis.