Public Opinion Supervision Research Articles

Public Service Competition，Factor Flow and New Regional Economic Pattern

In the past 40 years, local government competition has played a very important role in China’s economic growth. However, the rapid economic growth has been accompanied by challenges in public health, public education, resources and the environment, etc. How to improve the level of public services and gather innovative elements such as human and capital with a better capacity of carrying is a realistic problem that urgently needs to be considered. The outbreak of COVID-19 epidemic is a major test for the national and local governance system. While forcing the improvement of local public service capabilities, the competition for public services around the prevention and control of the epidemic becomes visible. To clarify the realistic background and institutional constraints that lead local competition to economic growth rather than public services, and to guide public services to become a new competition yardstick for local governments through new institutional arrangements, can provide new impetus for China’s regional coordinated development, which is also the internal requirement of the modernization of national governance for local government reform.Firstly, this paper adopts a two-region model to show that the failure of local public services to coordinate with economic growth is a natural result of local government behavior choices. Under the new incentive and restraint mechanism, it is possible to move from economic growth competition to public service competition. Secondly, based on the typical facts of local public services, around the current major adjustments in China’s productivity layout, the empirical tests find that the full flow of population and capital elements in the new era relies on the spatial network system with infrastructure and public services as carriers, and public services have become a carrier for central cities and urban agglomerations to attract core resources. Furthermore, from the central-local-social perspective, based on a two-regime spatial Durbin model with fixed effects, the results show that reforming the performance evaluation index system, expanding local government information disclosure, enhancing public opinion supervision and public participation, and stimulating the vitality of social organizations provide a realistic and feasible way to promote the establishment of a competitive relationship of “racing to the top” and “aiming high” to improve the overall public service level and promote the equalization of public services.

Ultra-efficient information detection on large-scale online social networks

As a powerful and low-cost instant information dissemination platform, large-scale online social networks (OSNs) play a pivotal role in shaping our modern information age. The efficient detection of wide-spreading information in OSNs is very important in many aspects including public opinion supervision, social governance, stock markets, counter-terrorism and presidential election. However, real-world OSNs have gigantic sizes and thus their full structural data are usually unavailable, making this problem extremely challenging. In this work, we illustrate the close mapping between efficient detection and optimal spreading from the perspective of network percolation theory. This analogy inspires us to propose a theory of using only limited local network information to select the optimal set of information sensors. Through extensive simulations on both synthetic and real-world networks, we find that for networks with theoretically infinite size, only a finite and small number of sensor nodes are needed to detect the global spreading information with almost certainty. Most importantly, we empirically confirm the utility of our theory on the largest micro blog in China by crawling almost the full Sina Weibo social network with 99,546,027 users in 2014 and the real spreading data of Weibo messages.

Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

FinTech innovation regulation based on reputation theory with the participation of new media

The information asymmetry caused by the virtual nature of the Internet is a challenge faced by FinTech innovation regulation, whereas new media participation makes the information environment more complex. From the perspective of finding a dynamic balance between innovation and regulation, this study considers reputation adjustment under new media participation, as well as the corresponding selection of an innovation path. It is found that the equilibrium strategy of an excessive innovative FinTech platform is directly related to its time preference. For platforms that pay more attention to the current interests, the high-pressure regulatory environment and public opinion supervision can effectively restrain excessive innovation, whereas for platforms that focus on long-term interests, appropriate relaxation of regulatory pressure and public opinion environment can encourage the platform to maintain its reputation by pretending to be compliant.

Speech Emotion Analysis of Netizens Based on Bidirectional LSTM and PGCDBN

In recent years, the application of speech emotion recognition (SER) in the supervision of Internet public opinion has received increasing attention. This study proposes a new SER algorithm to analyze the public opinion information of network platforms. Firstly, we extract different spectrum features from speech signals and combine them into frame level speech features. Then, we select conditional deep confidence network (CDBN) which has the ability to learn sequential features as the final classification model. We apply particle swarm optimization (PSO) and genetic algorithm (GA) during the fine-tuning stage of the CDBN to obtain more suitable optimal weights of the whole network, and propose the PSO-GA-CDBN (PGCDBN) model. Compare with the traditional back propagation (BP) algorithm, our training method accelerates the convergence speed of the network and improves the robustness and recognition performance of the network. In our experiment, we used the Chinese Academy of Sciences’ Institute of automation (CASIA) Chinese emotional corpus and self-collected Chinese speech datasets, which were collected from Sina Weibo, Tik tok and other online social media platforms. Compare with the popular emotion classifiers such as support vector machine (SVM), deep residual network (ResNet), long short-term memory (LSTM) neural network, DBN, our proposed PGCDBN achieves the best recognition results from both datasets. In addition, we use bidirectional LSTM before PGCDBN to further process the extracted speech features, and the result of bidirectional LSTM has stronger speech signal expression ability. The average recognition accuracy of this new hybrid deep learning model algorithm in two datasets is 98.67%, which can be used for the supervision of netizens’ opinions.

Research on Status Quo and Educational Countermeasures of Ideal and Belief for College Students in Guangdong

Facing the diversification of social thoughts and the complex and changeable international situation, it is necessary to seek new ways and new methods for college students' ideals and beliefs education. This project is based on investigation and research, analyzes the influencing factors of the ideals and beliefs of Guangdong college students, puts forward corresponding countermeasures for the existing problems, and solves the key problems of ideals and beliefs education. Specific countermeasures include: carrying forward the excellent Chinese traditional culture and strengthening the education of socialist core values; actively carrying out social practice activities and continuously enhancing the sense of social responsibility and dedication; changing the discourse method of ideal and belief education and actively using micro-media to carry out education; continuously optimizing the educational environment of ideals and beliefs and giving full play to the combined effect of multi-party education; strengthening the training of will and ability and giving full play to the self-education function of college students' ideals and beliefs; innovating the official WeChat platform education methods and using online public opinion supervision to strengthen guidance.

Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data

This article focuses on the multidimensional construction of the multimedia network public opinion supervision mechanism, puts the research on the background of the era of big data, and based on the analysis and definition of the difference between network public opinion and network public opinion, deeply summarizes the network public opinion in the era of big data. New features analyze the opportunities and challenges faced by online public opinion in the era of big data. Based on the rational construction of the index system, this paper studies the multimedia network public opinion evaluation and prediction algorithm. Existing network public opinion assessment and prediction algorithms have shortcomings in capturing the characteristics of data sequences and the long-term dependence of data sequences, and the problems of overfitting and gradient disappearance may occur during training. Because of the above problems, based on the long-term and short-term memory network model, a regularized method is used to construct a multimedia network public opinion prediction model algorithm. This paper builds a multimedia network public opinion threat rating evaluation model based on the public opinion supervision prediction model and conducts analysis. The model constructed this time can not only improve the accuracy of public opinion assessment and prediction but also better avoid the problem of gradient disappearance and overfitting.

Resent Situation and Cultivation Strategy of Network Media Literacy of “Micro-blog and WeChat -Era” Youth Generation

With the innovation of mobile internet technology and the popularization of smart phones, China enters a Micro-blog and WeChat Era. The government’s supervision of public opinion will also focus on micro-blogging, WeChat - messaging and other network media platforms. Youth is the main force of the emergence of network public opinion, and improving their media literacy is an important issue at present. This research provides countermeasures and suggestions for improving youth media literacy in the new era.

The Exploring on the Supervision of University Public Opinion in the Background of Big Data

College public opinion are a reflection of the ideological status of college students and the degree of campus stability. The high frequency and suddenness of public opinion events and the speed and virtuality of the information age have brought new opportunities and challenges to public opinion supervision and public opinion intervention. In the new era, the supervision of network public opinion in colleges and universities should conform to the characteristics of the era of big data, use advanced information technology to improve the efficiency of supervision, and strive to achieve the goal of scientific decision-making management. Based on the major colleges and universities, this paper aims to explore the improvement program of college public opinion supervision under the background of big data. It uses literature review method, research method and experimental method to analyse. This paper analyzed the current situation of network public opinion supervision in colleges, summed up the problems existing in the network of colleges, and constructed a network public opinion supervision platform for colleges under the background of big data.

舆论监督视角下探析执法规范化

舆论监督视角下探析执法规范化

Host location technology of campus network based on MAC address

With the increasingly strong nature of campus network teaching and service in the university, network management and security management are slowly catching up with the development of campus network construction. How to strengthen network security and public opinion supervision, how to locate the suspected computer location in the campus network has become an important problem. This paper proposes the campus network platform of colleges and universities as an example, expounds the process of computer physical location, analyzes the problems generated in the tracking process, and finally summarizes the application.

A Novel Identification Approach for Palm Bio-Impedance Spectroscopy

Aiming at the properties of the palm bio-impedance spectroscopy (PBIS) data and shortage of the existing algorithms, this paper proposed a kind of the PBIS identification method based on immune clone. This method mainly consisted of three parts, which are data normalization, feature selection and matching. The PBIS algorithm constructed a new classifier based on the immune clone selection algorithm (ICSA) and the hidden space support vector machine (HSSVM), called the ICSA-HSSVM. It was compared with other classifiers such as Euclidean distance, cosine distance and support vector machine (SVM). The experimental results show that the proposed classification method can achieve more than 95% accuracy in identity recognition. The PBIS biometric identification tech depends on the human body inherent characteristics to carry out the status identification so as to overcome the drawbacks of traditional information safety tech status identification. The main advantages of PBIS algorithm include four aspects. The first is the uniqueness, universality, stability, easy collection, and it is basically not being subject to the influence of pathological changes in the human body positions. The second is not sensitive to the environment and the excellent recognition performance can be obtained through the method easily. The three is high stability and uniqueness when it is served as the biometric potentiality, being in agreement with biometric identification requirements. (4) The four is able to resist against counterfeiting attacks effectively. The method proposed in this paper is of great theoretical and practical values in the fields of national public security, internet information security, military communication security, micro-blog micro-message public opinion supervision.

Sensitive Information Topics-Based Sentiment Analysis Method for Big Data

With the rapid development of the Internet, more and more users expressed their views on the Internet. Therefore, the big data of texts are generated on the Internet. In the era of big data, mining the sentiment tendencies contained in massive texts on the Internet through natural language processing technology has become an important way of public opinion supervision. In this paper, the sensitive information topics-based sentiment analysis method for big data is proposed. This method integrates topic semantic information into text representation through a neural network model. The attention mechanism is introduced into the neural network, and context-aware vector is introduced to calculate the weight of each word. In addition, in order to make the model more adaptable, the method of sentiment dictionary tagging is used to obtain the training data. The experimental results show that the proposed model can effectively improve the accuracy of sentiment analysis results.

Analysis on the Reasons of Tourists' Uncivilized Travel Behavior

The uncivilized travel behavior restricts the rapid development of tourism industry. This paper take the 10th China (Wuhan) International Garden Expo for example, to analyze the causes of tourists’ uncivilized travel behavior, and put forward some improvement measures, such as strengthen the guidance and supervision of public opinion, strengthen the supervision of the administrative departments, improve the basic facilities in scenic spots.

Rappresentanti dell'autorità e agenti del consenso: il ruolo di prefetti, clero e militari durante le feste civiche nel Regno d'Italia napoleonico

Mixing perspectives and methods typical of the cultural history of politics and of the history of public institutions, the author analyses the conduct of public officials in the Napoleonic Kingdom of Italy during the organisation of civic festivals. His aim is to enlighten on the one hand the self-representation of the Napoleonic political power, always looking for a complete legitimation, on the other hand the practices of development and supervision of an appropriate public opinion among the population. In this way the author shows that highly-symbolic festivals first of all represent a test of officials’ loyalty and reliability: as a matter of fact, they are essential delegates of the political authorities and mediators between them and local communities.

Public Opinion Supervision: A Case Study of Media Freedom in China

Since the late 1970s, the People’s Republic of China has experienced a rise of investigative journalism along with economic liberalization. Scholars contend that with the introduction of capitalism and the consequent relaxation of media control, a degree of real press freedom has crept into China’s society.4 In particular, scholars highlight a unique phenomenon known as yulunjiandu, or “public opinion supervision,” where citizen awareness of an issue is mobilized to act as a check against the state, to influence court decisions, or to push for legislative or policy reforms. The media plays a critical role in this process. Many perceive the media as a positive force in China through which the public can be heard, and some equate public opinion supervision to a form of media monitoring, where the media serves as an independent watchdog monitoring the government.

Study on public opinion propagation in self media age based on time delay differential model

With the emergence and popularization of computers, human society has entered the media age. On the one hand, it is beneficial to balance the supervision of public opinion and the construction of judicial responsiveness mechanism; on the other hand, false news increases the pressure of the government, and is not conducive to the establishment and enhancement of the government’s public trust. Therefore, it is very important for the whole society to study the law of the spread of Internet public opinion and then to control it.In this paper, we study the propagation law of Internet public opinion with the object of “ Xiongan New Area ”. Since the media network public opinion has the similar process of exponential growth in the communication process, and will be influenced by external capacity constraints, we establish the Logistic equation, introduce the τ operator time delay differential equation, and finally establish the improved delay differential equation, which can describe the propagation trend of network news from the self media. It provides certain reference for the government to study the development of public opinion.Click here and insert your abstract text.

Topical Influence Analysis Algorithm based on Information Propagation in Microblogs

With the development and popularization of the Internet, the different microblog topics are disscussed everyday, so the microblog can product a large number of various topics,which can reflect the influence of different users in a given topic. In the microblog topics,the key users of microblog topics are found by discovering the influential sensitive topics and calculating the influence value of the user,which are the focus of attention in the fields of microblog public opinion supervision and safety management. In order to accurately measure the influencers in the given topic and to calculate the user influence value, the thesis proposes the method of constructing the propagation network which is based on attention and forward relationship between users, and then proposes TDN-If algorithm by using PageRank algorithm. When we calculate the transition probability in propagation network by using the TDN-If algorithm, the information propagation is considered to measure influencers. This method can resolve the defects of discovering the user's influence by only using followers of this single indicator in the current microblog topics. The experimental results show that the TDN-If algorithm has important theoretical and practical value, which is better than TwitterRank algorithm and other influential individuals found algorithm. Thus, the method proposed in this paper can not only effectively solve the problem about discovering and persuading the key users in the sensitive topics who have influences and have the unique insights on the significant events, for example, which can provide the strong guarantee for the governments in the fight against terrorism, but also provide the important theory and method for the complex network community discovery, microblog public opinion supervision , microblog safety management and so on.

The Analysis of Network Public Opinion Monitoring Management System Based on B/S Architecture

Public opinion monitoring system takes advantage of the search engine technology and the network infor- mation mining technology, through the contents of the Web page such as automatic acquisition and processing, sensitive word filtration, intelligent clustering and classification, topic detection, thematic focus, statistical analysis. For these con- tents, every unit needs to be under the network of public opinion supervision and management system and its practical value is eventually formed as a result of public opinion presentation, public opinion, the special report, analysis report, and decision layer to have a comprehensive grasp of public opinion trends, which provides a correct guidance for the pub- lic opinion measurement, and supplies the basis for the analysis. A public opinion monitoring system has been introduced in this paper, which is based on the dynamic kernel with the structure of B/S for the demonstration in the form of the software system. Combined with the actual market demand, it works through the effective technology of theoretical medi- cine and the implementation of a professional development team so as to ultimately achieve the goal of monitoring and control system of public opinion.

The Battle of Microblogging for Legal Justice in China

Microblogging has a special appeal not only to Internet users in China who are keen to voice their opinions in a fast and easy way, but also to Chinese rulers who see microblogging sites as a giant magnetic field for them to tap into and to mold public opinions. In fact, since the 1980s, “public opinion supervision” has been on the agenda of the Chinese Communist Party. Yet, by the time of the 21st century, armed with the Internet, public opinion supervision has evolved into a powerful phenomenon with a life of its own. At its present time, the strange twist of logic is that the press is still tightly controlled by the Community Party but public opinion has been roaming wildly on the Internet, and creeping into the court rooms. To capture the above dynamic, this paper studies the top 20 events discussed in blogs, microblogs and online discussion boards in China between 2008 and 2012, and analyses the extent that public opinion has influenced the investigation and judicial outcome of cases. This article focuses on the intricate relationship between the Party, the judiciary, the Internet and the netizens. It argues that the Internet has transformed public opinion supervision into a form a public opinion monitoring. The prowess of the netizens lies not in their ability to gather information but in being an active power to interpret, to associate and to transform the plight of their fellows into legal narratives. One of the biggest challenges now, perhaps, is to ensure public opinion can be a form of monitoring, untainted by the supervision of the authorities.