Internet Public Opinion Research Articles

Exploring the Innovative Path of Comprehensive Management of Dynamic Online Public Opinion in China Under the Threshold of New Media

Internet public opinion is a dynamic process of change, which is characterised by hiddenness, suddenness, complexity and volatility. The governance of online public opinion is a systematic project that involves network communication, government governance, social organisations, public will and other subjects. Strengthening the comprehensive governance of online public opinion requires innovating governance concepts and mechanisms to solve the problem of online public opinion at source; strengthening institutional construction and collaborative governance to improve the mechanism for managing online public opinion; enhancing professional skills and media literacy to improve the ability to govern online public opinion; and innovating information dissemination means and methods to enhance the dissemination efficacy of online public opinion. By innovating governance concepts and mechanisms, integrating social resources, improving the construction of the network communication system, and enhancing the media literacy and professional skills of netizens, the capacity and level of network public opinion governance will be enhanced.

Time varying characteristics of factors affecting carbon price in emission trading scheme in China: Evidence from SV-TVP-VAR approach

Analysis of the factors influencing the price of carbon emissions trading in China and its time-varying characteristics is essential for the smooth operation of the carbon trading system. We analyse the time-varying effects of public concern, degree of carbon regulation, crude oil price, international carbon price and interest rate level on China’s carbon price through SV-TVP-VAR model. Among them, the quantification of public concern and the degree of carbon emission regulation is based on microblog text and government decisions. The results show that all the factors influencing carbon price are significantly time-varying, with the shocks of each factor on carbon price rising before 2019 and turning significantly thereafter. The short-term shock effect of each factor is more significant compared to the medium- and long-term, and the effect almost disappears at a lag of six months. Thanks to public environmental awareness, low-carbon awareness and the progress of carbon market management mechanisms, public concern has had the most significant impact on carbon price since 2019. With the promulgation of relevant management measures for the carbon market, relevant regulations on carbon emission accounting, financing constraints, and carbon emission quota allocation for emission-controlled enterprises have become increasingly mature, and carbon price signals are more sensitive to market information. The above findings provide substantial empirical evidence for all stakeholders in the market, who need to recognize that the impact of non-structural factors on the price of carbon varies over time. Government intervention also serves as a key aspect of carbon emission control and requires the introduction of relevant constraints and incentives. In particular, emission-controlling firms need to focus on the policy direction of the carbon market, and focus on the impact of Internet public opinion on business production while reducing carbon allowance demand and energy dependence.

The Way Forward for Online Public Opinion Governance in the Age of Big Data

With the rapid development of big data technology and the popularization of Internet applications, the role of Internet public opinion in social governance has become increasingly prominent. Internet public opinion not only reflects hot issues of social concern and public sentiment, but also affects social stability and national security. Therefore, strengthening the rule of law governance of online public opinion has become an urgent problem. The purpose of this study is to explore the rule of law basis and major problems of online public opinion governance in the era of big data, to analyses the shortcomings of the current implementation of the rule of law in online public opinion, and to propose corresponding countermeasures. In the context of the big data era, the rule of law in online public opinion governance faces challenges in many aspects. Inadequate supervision, imperfect rule of law system, deficiencies in the implementation mechanism, and weak public awareness of the rule of law together constitute the main problems in the current governance of online public opinion. To effectively deal with these problems, it is necessary to start with strengthening supervision, improving the rule of law system, reinforcing the implementation mechanism and raising awareness of the rule of law. Through these measures, a more mature and effective rule of law system for the governance of online public opinion can be constructed, thereby safeguarding the healthy development of the online information environment and maintaining social harmony and stability.

An Empirical Study on the Causes of College Students' Participation in Internet Public Opinion and Its Countermeasures—Based on Data from Colleges and Universities in Harbin

An Empirical Study on the Causes of College Students' Participation in Internet Public Opinion and Its Countermeasures—Based on Data from Colleges and Universities in Harbin

Legal and Economic Research on Internet Public Opinion and Justice —Jiang Ge Case and Yao Jiaxin Case from the Perspective of Comparative Law

Legal and Economic Research on Internet Public Opinion and Justice —Jiang Ge Case and Yao Jiaxin Case from the Perspective of Comparative Law

Research on the Governance of Internet Public Opinion in Emergency Events in Universities

Research on the Governance of Internet Public Opinion in Emergency Events in Universities

The Impact of Internet Public Opinion on Justice —Jiang Ge and Yao Jiaxin Cases in the Perspective of Comparative Law

The Impact of Internet Public Opinion on Justice —Jiang Ge and Yao Jiaxin Cases in the Perspective of Comparative Law

A study of internet public opinion leaders with COVID-19 pandemic in Taiwan as a case

A study of internet public opinion leaders with COVID-19 pandemic in Taiwan as a case

Managing internet public opinion in colleges and universities in China: Towards the humanization, ethics and responsibility in educational guidance

Managing internet public opinion in colleges and universities in China: Towards the humanization, ethics and responsibility in educational guidance

Leaderboards and Top Public Opinions: A Study of Discourse Conflict in the Internet Public Opinion Field

In the era of new media, with the continuous development of Internet technology, social media and online public opinion fields have become an existence that cannot be ignored. Frisbee, as a niche sport in North America, has become popular on social platforms. This sport also breaking the circle to become a hot topic in the online public opinion field , and discourse conflicts around the "frisbee phenomenon" has unfolded. Based on the theoretical background of ranking effect, value accumulation theory and agenda setting, this paper innovatively combines the network fieldwork method with the interview method. We find that the topic leaderboard mechanism is the primary promoter of the "breaking circle" behavior of Frisbee, the platform topic hot search mechanism breaks the "echo wall", and the technology availability promotes the development of "re-tribalization". Due to their own differences in experience, all the discussants clash with each other, and irrational expression leads to the fermentation of online public opinion and discourse conflict.. Discussers from all sides have a conflict of views due to their own differences in experience, and irrational expressions lead to the fermentation of online public opinion and discourse conflicts. This paper also actively explores the impact and solutions of discourse conflict, and provides a reference for future related research.

Internet Public Opinion on Media Report Semantics of U.S. House Speaker Nancy Pelosi’s Visit to Taiwan

On August 2, 2022, the U.S. House Speaker, Nancy Pelosi visited Taiwan. China expressed strong dissatisfaction, triggering the 4th Taiwan Strait Crisis. We investigated the differences in the frameworks constructed by the news media and the public opinion about the “event of Nancy Pelosi’s visit to Taiwan”. Semantics and “public opinions” associated with the report contents of Nancy Pelosi’s visit to Taiwan were analyzed using the online news media of major newspapers in Taiwan, as well as the social media PTT Gossiping. The Python programming language was used for web crawling, and the word frequency database snowNLP in Python was adopted as a sentiment analysis tool. The research results showed that the emphasis of different media in different periods was slightly different. The topics of netizens in PTT Gossiping were often affected by the topic framework of the news media. However, the public developed their discussion topics. News media reports did not have a framing effect on the emotional changes of common words used by the public about the issues.

Analysis of Public Opinion Crisis Control by Mainstream Media During COVID-19

The sudden outbreak of new pneumonia has had a profound impact on the global world and all walks of life. At the same time, it also creates a torrent of public opinion under the mud of information and accelerates the fermentation and loss of control of the Internet public opinion field. Tuchman, a famous scholar, claimed in Doing the News that news is the constructed reality, and the mainstream media controls the trend of public opinion when disseminating news. In this paper, public opinion at each stage of the epidemic was analyzed based on the crisis phase analysis theory proposed by Steven Fink. In the face of the epidemic crisis, the mapping of the media plays an important role. Mainstream media should control the public opinion crisis through different discourse strategies and social effects caused by communication methods.

Analysis of civil and commercial law protection based on decentralized Internet

Abstract To promote fairness and justice in law, it is important to explore the protective role of civil and commercial law. The relationship between civil and commercial law and public opinion supervision is examined through Internet public opinion. A decentralized Internet public opinion index system has been established, and a better random network public opinion dissemination model is proposed through parameter inversion of the SIR model and network simulation. The protection of transaction security and information rights by civil and commercial law is the focus of the evolution of Internet public opinion cases. The promotion of industry self-regulation is enhanced by 14.63% when civil and commercial law intervenes. On the flip side, it aids in protecting personal information, and the likelihood of personal information leakage is reduced by 25.48%. This study can effectively reference the implementation of civil and commercial law in the Internet era.

Retracted: The Spread, Rise, and Fall of University Students' Interconnected Internet Public Opinion in the Age of Big Data.

[This retracts the article DOI: 10.1155/2022/9196913.].

Sentiment analysis and prediction model based on Chinese government affairs microblogs

Existing sentiment analysis research on Chinese government affairs microblogs primarily focuses on the task of sentiment classification on microblogs. There has been a lack of investigation into the correlation of each government affairs microblog with the sentiment values of the corresponding comments below it. This study constructs a large-scale government affairs microblog dataset and explore the correlation of each microblog with the sentiment values of the corresponding comments below it. We proposed a new framework that includes data collection, sentiment analysis and sentiment prediction model training. This sentiment analysis framework is crucial in the government's understanding of the public's real-time sentiments toward policies. It also helps monitor the Internet public sentiment and actively guide the Internet public opinion. We first analyzed the sentiment distribution of government affairs microblogs and the sentiment values on meaningful words. We also discussed the discrepancy in text similarity and sentiment values between microblogs. Furthermore, we investigated the extreme emotional content and discussed the factors influencing the sentiment values of comments. Finally, we designed a collaborative attention regression model to predict the sentiments of microblogs. The sentiment prediction model performed well in the sentiment prediction regression task. The sentiment analysis and the prediction framework for government affairs microblogs in this study can be used as a reference for government-related Internet opinion monitoring.

Research on the Influence of Internet Public Opinion in Weibo on the Development of Teenagers' Mental Health

Because of the unbalanced physical and mental development of teenagers, they are faced with more contradictions and conflicts. While enjoying the benefits of network technology, they have quietly become "slaves" of the network, and even formed mental health problems of different degrees. Weibo's network enriches teenagers' emotional experience, which is conducive to teenagers' understanding of society. Weibo platform has a large number of users, which provides a more convenient way for public opinion, becomes the main force of online public opinion, and has a certain impact on social development. Because of the unbalanced physical and mental development of teenagers, they are faced with more contradictions and conflicts. While enjoying the benefits of network technology, they have quietly become "slaves" of the network, and even formed mental health problems of different degrees. Therefore, only by mastering the operating rules of Weibo's public opinion ecosystem can we effectively monitor online public opinion, establish timely early warning and respond to the influence of online public opinion on teenagers' mental health as soon as possible.

MTR-SAM: Visual Multimodal Text Recognition and Sentiment Analysis in Public Opinion Analysis on the Internet

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.

Structural Analysis of the Evolution Mechanism of Online Public Opinion and its Development Stages Based on Machine Learning and Social Network Analysis

Internet public opinion is a complex and changeable system, and its trend development is characterized by explosive, evolutionary uncertainty, concealment and interactivity due to the participation of the vast number of Internet users. Today, with the rapid development of network information technology, public opinion has an increasing influence on the stable development of society. Computational intelligence is the frontier field of artificial intelligence development, and computational intelligence is used to mine and analyze public opinion text information and study the evolution of online public opinion. This paper uses the Changchun Changsheng Vaccine Incident as an example, and the netizens’ degree of attention to emergency-related keyword searches in the Baidu Index as a descriptive variable for the development of network public opinion. After applying the optimal segmentation algorithm, the development of public opinion is divided into phases. On this basis, a social network analysis is adopted to analyze the spatial and topological structure of each phase of network public opinion, using data from the Sina Weibo platform. Based on optimal segmentation, the development of network public opinion of the Changchun Changsheng Vaccine Incident can be divided into four phases, namely latent, spreading, control, and stable; each phase has different spatial and topological characteristics. Corresponding policy suggestions on network public opinion governance are put forward for each phase.

Research on endogenous internet public opinion dissemination in Chinese universities based on SNIDR model

During an emergency, the negative Internet public opinion in colleges and universities, especially the negative endogenous public opinion, will have a serious impact on the reputation of colleges and universities. It is of great significance to find out the negative influencing factors of endogenous public opinion and explore the mechanism of public opinion dissemination for resolving the crisis of public opinion in universities. The existing research does not distinguish the endogenous Internet public opinion in colleges and universities from the general Internet public opinion in colleges and universities, and the SIR model adopted fails to fully reflect the difference between students and other dissemination subjects of endogenous public opinion in campus. In addition, various research methods and models currently used focus on the static expression of dissemination results, and the explanation of results is insufficient. The reason is that they do not well express the dynamic interaction mechanism between influencing factors and the dynamic conversion rate between roles. In this study, based on the improved infectious disease model and system dynamics theory, AnyLogic software is used to simulate the improved SNIDR model of infectious disease, to analyze the sensitivity of school supervision, school intervention, school response time and information transparency and to study the dynamic conversion rate between different roles. The SNIDR model effectively simulates the process of endogenous public opinion dissemination in colleges and universities after emergencies. The results show that, what has the greatest impact on the dissemination of public opinion is the school’s supervision and intervention efforts, which can suppress the dissemination from the source. Information transparency is an auxiliary variable and cannot function independently. During the dissemination period, the timelier the school responds, the faster the spreaders will drop to zero, and the better it will be to control the secondary dissemination of public opinion.

Positive public opinion guidance model based on dual learning in social network

Internet public opinion is closely related to our life in social network. The wanton growth of some negative public opinions has an extremely serious impact on the social stability and national security. After the guidance of government manually, some negative public opinion is well controlled and people’s life gain more positive energy. How to use Internet technology to automatically and promptly guide public opinion events and reduce the harm to society is currently challenging research. Therefore, in this paper, we propose a positive public opinion guidance model based on dual learning for negative Internet public opinion, hereinafter denoted to as the dual-PPOG model. Firstly, we use the Fast Unfolding algorithm to divide social networks into the public opinion guidance communities. In these communities, we detect the positive opinion guider and negative opinion receiver by our defined PageRank (PR) variant. Secondly, inspired by dual learning, we construct the public opinion guidance model and evaluate whether the guidance is successful through the feedback signal. Through the repeated guidance of the positive opinion guider to the negative opinion receiver, the public opinion guidance is successful. This is the main process for the dual positive public opinion mechanism. Finally, we guide the remaining nodes based on the opinion dynamics. The experiment demonstrates beneficial effects of our proposed model of dual-PPOG. Experimental results on three real-world datasets intercepted from Twitter, E-mail and Facebook show that the model of dual-PPOG can capture useful information in the network topology. Compared with the methods of HK, AE, Random and AIA on the three datasets from small to large in scale, the percentage of positive opinion increased by 4%, 6.9%, and 2.7% respectively, which shows our approach achieve significant improvements and effectiveness compared to all baselines.