Public Opinion Crisis Research Articles

Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks

People publish tweets on Twitter to share everything from global news to their daily life. Abundant user-generated content makes Twitter become one of the major channels for people to obtain information about real-world events. Event detection techniques help to extract events from massive amounts of Twitter data. However, most existing techniques are based on Twitter information streams, which contain plenty of noise and polluted content that would affect the accuracy of the detecting result. In this article, we present an event discovery method based on the change of the user’s followers, which can detect the occurrences of significant events relevant to the particular user. We divide these events into categories according to the positive or negative effect on the specific user. Further, we observe the evolution of individuals’ followership networks and analyze the dynamics of networks. The results show that events have different effects on the evolution of different features of Twitter followership networks. Our findings may play an important role for realizing how patterns of social interaction are impacted by events and can be applied in fields such as public opinion monitoring, disaster warning, crisis management, and intelligent decision making.

A Intelligent Evaluation Model Framework of Online Public Opinion Crisis in Higher Vocational Colleges

Higher vocational colleges should establish a real-time online public opinion monitoring mechanism. Before deterioration trend occurs, take relevant measures to relieve and guide. The current ability to deal with online public opinion crisis needs to improve. This paper puts forward the principle of the warning index system of the online public in higher vocational colleges, designs a new evaluation index system, builds the intelligent evaluation model framework based on AHP, fuzzy evaluation method and BP neural network which can effectively learn the knowledge and experience of evaluation experts. This model framework can make full use of available data in the big data environment and reduce manpower.

Analysis of the reflection case of major hazardous chemicals safety accident based on LFI

After the occurrence of major safety accidents, the key to prevent similar accidents from happening again is whether all sectors of society can get deep reflection from the investigation and accountability of accidents, and then improve the level of safety awareness and safety management and control through the process of learning from incident (LFI). Taking “Tianjin Port 8.12 major dangerous chemical explosion accident” as an example, 610 articles related to the accident were exported from CNKI as samples. The topic classification of the abstract text was carried out by using LDA topic model. Finally, based on the theory of learning from incident (LFI), the evolution trend and content of the topic of reflection were analyzed. The results show that: (1) the content of reflection mainly includes the following four topics: (I) Risk of hazardous chemicals and safety of enterprises; (II) Emergency management and rescue; (III) Government public crisis management; (IV) Public opinion crisis in emergencies (2) the proportion of research on topic II is lower than that of other topics, and there may be insufficient reflection on individual issues. As it has the function of reducing accident harm, it is of great significance and should be given enough attention.

Analysis of microblog public opinion based on big data technology

With the advent of new technology era, big data technology has been widely used in the public communication of mainstream media and become an important strategy. With the continuous progress of the times, big data has entered thousands of households. The arrival of the mainstream media in the new era has changed the traditional public opinion environment, and also brought new challenges to the mainstream media how to deal with the sudden public opinion correctly. Sudden public events are easy to cause public opinion, which is easy to spread, ferment, and even lead to the aggravation of social contradictions. In the field of public communication vision, this paper analyzes the characteristics of public communication of new mainstream media, points out that the new mainstream media face the challenge of news release and public opinion guidance in the current major emergencies, focuses on the analysis of the public opinion guidance effect of people’s daily micro blog in the communication process of Changchun long life vaccine event, and expounds the public communication of the current official public opinion field in new media What should be done in broadcasting. Under the influence of public opinion crisis, how the new mainstream media to use the communication strategy to guide the citizens’ network participation scientifically and correctly, and then solve the social contradictions efficiently has become an important topic.

The damped oscillator model (DOM) and its application in the prediction of emotion development of online public opinions

Online public opinions refer to the attitude of the public towards certain events or topics in social media and have informative significance for social governance and the formulation of public policies. Previous studies have proved the important role played by online public opinions in expert systems from which the prediction of online public opinions has drawn increasing attention from scholars. Though the development of public opinion events is largely driven by the emotions of users, few studies have regarded the issues of emotion development forecasting, and existing emotional dynamic models are relatively simple, not real-time predicting, and with no considerations of people's self-emotion-changing mechanisms. In order to fill this gap, this paper proposes a Damped Oscillator Model (DOM). Compared to existing models, the proposed model has two main merits: first, the Mass-Spring-Damper system in the physics area is used to measure the self-decaying process of human emotion which has not been included in previous opinion dynamics models; second, a self-emotional adaptation mechanism is introduced and final states with no obviously established opinions can be reached, which is common in real online public opinion cases. Simulation experiments are conducted to discuss the impacts of critical parameters contained in the model on opinion dynamics. Two real online public opinion events were studied using the proposed model, with critical parameters extracted by the particle swarm optimization algorithm. The predictive results have outperformed several previous well-known models. With its real-time predictive capacity, this model can provide substantial auxiliary support to the expert and intelligent system for making wise decisions in advance, especially during fast developing public opinion crises.

Effectiveness of Trust Repair Strategies in the Crisis of Corporate Internet Public Opinion

In the Internet environment, the network public opinion of corporate quality defects will cause enterprises to fall into the crisis of consumer trust. Enterprises often choose to issue announcements to deal with public opinion. Can an announcement release effectively restore trust? Are there differences in the effectiveness of different strategies in the announcement? It has become an urgent problem to be explored in the management of corporate public opinion crisis. This study takes the three corporate defect events exposed at the March 15th Evening Party as cases, and crawls the online comments and corporate response announcements on the microblogs about defect events. First, the sentiment analysis is used to calculate the emotional strength of the defect events. Secondly, the content analysis method is used to classify the trust repair strategies of the enterprise announcements. Finally, by constructing the trust repair strategy effectiveness model, the effectiveness of the trust repair strategy adopted by enterprises in different evolution stages is analyzed. The results show that in different stages of public opinion evolution, the release of response announcements can effectively enhance the emotional intensity of consumers. Moreover, in the emergency period, the relationship repair strategy is more effective; in the sustained period, the informational repair strategy is more effective; in the regression period, the functional repair strategy is more effective. Through practical cases, the research explores the effectiveness of enterprise trust repair strategy under the network public opinion crisis, expands the research content of enterprise network public opinion field, and provides strategic advice for enterprise public opinion crisis management.

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

Fake reviews may mislead consumers. A large number of fake reviews will even cause huge property losses and public opinion crises. Therefore, it is necessary to detect and filter fake reviews. However, most existing methods have lower accuracy in detecting fake reviews due to they just use single features and lack of labeled experimental data. To solve this problem, we propose a novelty method to detect fake reviews based on multiple feature fusion and rolling collaborative training. First, the method requires an initial index system with multiple features such as text features, sentiment features of reviews and behavior features of reviewers. Second, the method needs an initial training sample set. Thus, we designed related algorithms to extract all the features of a review. Then the classification of the review is labeled manually. Finally, the method uses the initial sample set to train 7 classifiers, and the most accurate classifier will be selected to classify new reviews. The novelty of the method lies in that the features and the classification labels of the new reviews will be added into the initial sample set as new samples. So the size of the sample set will increase automatically. The experimental results in the reviews of yelp shopping website show that the accuracy of the proposed method for detecting fake reviews is 84.45%, which is 3.5% higher than the baseline methods. And compared with the latest deep learning model, its baseline precision has increased by 5.3%. According to the Friedman test, the support vector machine (SVM) classifier and random forest (RF) classifier has been proven to be the best one by statistical means. It means our method which uses multiple features has higher accuracy than the baseline models. Meanwhile, it also resolves the problem of lacking labeled training samples in fake reviews detection.

A Systems Dynamics Simulation Study of Network Public Opinion Evolution Mechanism

The factors that affect formation and dissemination of public opinion have been studied for a long time. However, the findings are disparate and fragmented, given the characteristics of netizens and new media in the Big Data era. To this end, this article introduces eight mechanisms working on formation and dissemination of public opinion on network. Based on system dynamics, this article further proposes a comprehensive causal relationship model to explore the factors affecting the consequence of public opinion on network. Particularly, the role of government is taken into consideration in this model. A simulation with Vensim PLE is conducted. The results of the simulation indicate that group polarization among netizens, opinion leaders, the quantity of media audience, the frequency of media report, government attention, and warning mechanism for public opinion crisis affect the consequence of public opinion on network significantly. Implications of the findings are discussed.

Public Management Innovation in the Age of Big Data

In the Internet age, computer technology and data analysis technology have been applied to the daily lives and work of the people. Big data technology has brought great influence to public management, providing efficient and convenient public services and improving the ability to cope with public opinion crises [1]. However, in the actual public management process, there are widespread problems such as single practice and poor data openness. Based on this, the article expounds the relevant content of big data, introduces the role of big data in public management, and studies the public management innovation in the age of big data.

A New Decision Method for Public Opinion Crisis with the Intervention of Risk Perception of the Public

Decision‐making for selecting response plans problem (SRPP) has been widely concerning to scholars. However, most of the existing studies on this problem are focused on public emergencies, and little attention has been paid to the decision‐makers’ urgent need for solving the SRPP in response to public opinion crisis (POC) that may lead to panic buying of materials derived from public emergencies. POC has obvious characteristics of group behaviors that directly resulted from panics and psychological appeals of the public. Therefore, for solving the SRPP in POC, it is necessary to consider the deep‐seated cause that result in panics and psychological appeals of the public, i.e., risk perception of the public (RPP). Firstly, the multicase study is employed to describe the SRPP of POC, and thus eight typical cases are chosen to analyze POC and its relevant response measures. Then, the RPP is described with prospect theory through considering the behavioral characteristics and critical sense of the public, the response measures of decision‐makers, and the importance and ambiguity of POC. Further, considering the behavioral characteristics of decision‐makers and the impact of alternative response plans on the evolution of POC scenarios, a new decision method for solving the SRPP with the intervention of the RPP is proposed by using cumulative prospect theory and a manner of comparing alternatives for each other. Finally, an example is given to illustrate the potential application and effectiveness of the proposed method.

Detecting opinion evaluation object drift in Chinese microblog posts

ABSTRACTThe drift of opinion evaluation object can reflect the evolution of online public opinion from a fine‐grained level. By representing and calculating the distribution of opinion evaluation objects, we propose a method to detect its drift. A dataset of posts published by opinion leaders in social‐events discussion is collected from a microblog application in China. The preliminary result on the dataset shows that when subject discussion changes, opinion leaders will focus on the identity characteristic of one person in most cases. By using our proposed method, the drift of opinion evaluation object can be clearly uncovered. Furthermore, by combining with opinion sentiment tendency drift, our research can enrich the perspective of public opinion evolution analysis and better the monitoring of public opinion crisis.

Opinion data mining based on DNA method and ORA software

Public opinion, especially the online public opinion is a critical issue when it comes to mining its characteristics. Because it can be formed directly and intensely in a short time, and may lead to the outbreak of online group events, and the formation of online public opinion crisis. This may become the pushing hand of a public crisis event, or even have negative social impacts, which brings great challenges to the government management. Data from the mass media which reveal implicit, previously unknown, and potentially valuable information, can effectively help us to understand the evolution law of public opinion, and provide a useful reference for rumor intervention. Based on the Dynamic Network Analysis method, this paper uses ORA software to mine characteristics of public opinion information, opinion topics, and public opinion agents through a series of indicators, and quantitatively analyzed the relationships between them. The results show that through the analysis of the 8 indexes associating with opinion data mining, we can have a basic understanding of the public opinion characteristics of an opinion event, such as who is important in the opinion spreading process, the information grasping condition, and the opinion topics release situation.

A BDI-Agent Based Model for Public Opinion Crisis Response

A BDI-Agent Based Model for Public Opinion Crisis Response

A Quick Emergency Response Model for Micro-blog Public Opinion Crisis Based on Text Sentiment Intensity

On the basis of discussing the information spreading mechanism under Internet environment, we have studied on how to build a public opinion monitoring model according to the semantic content or text mining in recent years. A micro-blog public opinion corpus named MPO Corpus on the content of micro-blog information as a test data set has been constructed by our research team. In this paper, it proposes a quick emergency response model (QREM) for micro-blog public opinion crisis oriented to Mobile Internet services. Firstly, it describes the micro-blog cases and emergency response plan library using web ontology language (OWL), which makes the transitive logical reason capacity among micro-blog subjects, micro-blog cases and emergency plans. Secondly, it proposes an algorithm to calculate the sentiment intensity of micro-blogs from three levels on words, sentences and documents based on HowNet Knowledge-base respectively. Thirdly, we continue to study on how to update cases under the subjects and quick response processes for micro-blog case base. Finally, we design a test experiment which shows some merits of QREM in time, which basically meets the quick emergency response demand on the micro-blog public opinions crisis under Mobile Internet environment. Thus, it will provide more efficient support to the government and related monitoring departments involved with the public opinions crisis.

Tears of Rage: Chinese Nationalist Reactions to the Belgrade Embassy Bombing

Previous articleNext article No AccessTears of Rage: Chinese Nationalist Reactions to the Belgrade Embassy BombingPeter Hays GriesPeter Hays Gries Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The China Journal Volume 46Jul., 2001 Published on behalf of the Australian Centre on China in the World at the Australian National University Article DOIhttps://doi.org/10.2307/3182306 Views: 345Total views on this site Citations: 29Citations are reported from Crossref Copyright 2001 The China JournalPDF download Crossref reports the following articles citing this article:James P. 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