ABSTRACT
Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.
ABSTRACT
Online public opinion is a collection of netizens' emotions, attitudes, opinions, opinions and so on. With the development of the Internet, the influence of online public opinion on social stability is increasing day by day. This paper takes the 'COVID-19' event as an example, crawls the relevant news and comment data released by People's Daily, and firstly divides public opinion events into four stages according to the news popularity and life cycle theory: Tf-idf algorithm is used to strengthen the selection of key feature words in the corpus. Finally, LDA theme model is used to identify the topic of public opinion and mine the evolution law of network public opinion, which is helpful to effectively guide and control network public opinion and plays an important role in social stability. © 2022 IEEE.
ABSTRACT
In the age of big data, online public opinions breed and erupt when health emergencies occur. Tourism destinations have attracted much attention because of their unique high traffic and frequent population movements. It is crucial to take reasonable measures to cope with the outbreak of negative public opinion during the COVID-19 Pandemic. This paper uses Python to crawl the sentiment perceptions of tourists towards Tourism destinations during public health emergencies and classifies the sentiment as the dataset. Then, using Netlogo software to build an online opinion model, we simulate four scenarios for what a tourist destination should do to reduce the outbreak of negative public opinion: the release of information by opinion leaders, the change in the number of people contacted by negative public opinion, the change in the speed of dissemination of negative public opinion, and the release of relevant policies. In the four scenarios, it was found that the scenario in which relevant departments issued regulations have the greatest impact on negative public opinions. Changing the speed of public opinion dissemination is the least significant scenario. © 2022 IEEE.
ABSTRACT
Due to the outbreak of COVID-19 in early 2020, a flood of information and rumors about the epidemic have filled the internet, causing panic in people’s lives. During the early period of the epidemic, public welfare information with active energy had played a key role in influencing online public opinion, alleviating public anxiety and mobilizing the entire society to fight against the epidemic. Therefore, analyzing the characteristics of public welfare communication in the early period can help us better develop strategies of public welfare communication in the post-epidemic era. In China, Sina Weibo is a microblog platform based on user relationships, and it is widely used by Chinese people. In this paper, we take the public welfare microblogs released by the Weibo public welfare account “@微公益” (Micro public welfare) in the early period of the epidemic as the research object. Firstly, we collected a total of 1863 blog posts from this account from January to April in 2020, and divided them into four stages by combining the Life Cycle Theory. Then the top 10 keywords from the blog posts of different stages were extracted using word frequency statistics. Finally, the LDA topic model were utilized to find out the topics of each stage whose characteristics of public welfare communication were analyzed in detail. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Under the situation of the normalization of the prevention and control of COVID-19, related online public opinion occurs from time to time. University administrators must grasp the right of online discourse to guide the direction of online public opinion and ensure the stability of campus order. This paper analyzes the necessity and feasibility of university administrators to grasp the right of online discourse from the basis of reality, compares two kinds of measures and their combinations through questionnaires and computer simulation experiments: publishing authoritative information and focusing on opinion leaders, argues the effectiveness of these two types of measures, and puts forward specific countermeasure suggestions on this basis. © 2022, The Author(s).
ABSTRACT
As the Internet becomes the main source of information for the public, grasping the emotional polarity of online public opinion is particularly important for relevant departments to supervise online public opinion. In order to more accurately determine the emotional polarity of public opinion in the epidemic, this paper proposes a public sentiment analysis model based on Word2vec, genetic algorithm and Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm. The Word2vec model converts the comment text into an n-dimensional vector, uses the Bi-LSTM algorithm to analyze the sentiment polarity, and uses the genetic algorithm to analyze the number of Bi-LSTM layers and the number of fully connected layers and the number of neurons in each layer of Bi-LSTM optimization. The experimental results show that the accuracy of the above model is compared with the accuracy of the Word2vec model and the LSTM model separately, and the accuracy is increased by 11.0% and 7.7%, respectively. © 2022 ACM.
ABSTRACT
In recent years, public security emergencies such as COVID-19, rainstorm and waterlogging have occurred frequently, posing a great threat to personal and property safety. At the same time, the panic buying behaviour induced by public security incidents, i.e., synchronous buying behaviour, further strengthens the harm caused by the event. For example, simultaneous panic buying of face masks amid the pandemic has sparked fears of a shortage. The food rush after the rainstorm caused food shortage in some groups and the price soared, which easily led to the extreme behaviour of the people. Based on this, this paper constructs a game model of synchronous buying under public emergencies, analyses the psychological incentive of synchronous buying behaviour and the formation mechanism of synchronous panic buying behaviour, and puts forward effective measures to alleviate synchronous buying behaviour. Finally, we verify the model and its application through an actual case. Copyright © 2021 Inderscience Enterprises Ltd.
ABSTRACT
China is the world's largest consumer and importer of pork. In the context of COVID-19, countries have implemented strict import inspection and quarantine standards, and pork imports are facing more complicated customs clearance procedures, resulting in a sharp increase in customs risks. Pork, as a basic livelihood product, has always been a sensitive topic on the Internet. Their public opinions often have an important impact on customs policies and are one of the important sources of customs risks. Based on Internet text big data mining and LDA-GRA analysis method, this paper classifies online public opinion on pork import during the COVID-19 pandemic into different topics, and conducts correlation analysis on public opinion text and customs policy, investigates the correlation between online public opinion, customs policy and customs risk, as well as its correlation strength. The results show that the online public opinion of pork import has a significant impact on the implementation of the customs policy, and causes a variety of potential customs risks of pork import. Pork import-related enterprises should strengthen public opinion monitoring to reduce losses caused by customs risks. © 2021 ACM.