Public Opinion Monitoring Research Articles

Information security and electronic public opinion monitoring of security risks in social networks towards individuals and organizations

Information security and electronic public opinion monitoring of security risks in social networks towards individuals and organizations

The Dissemination and Evaluation of Campus Ideological and Political Public Opinion Based on Internet of Things Monitoring.

With the advent of the information age, the rapid development of Internet of Things technology makes monitoring methods more flexible and changeable. As an intelligent application, the development of the Internet of Things brings convenience to public opinion monitoring. However, at present, due to the high price of transmission equipment, inconvenient maintenance, information delay, and other unfavorable conditions, real-time and controllable public opinion monitoring cannot be carried out on a large scale, and there are still many deficiencies in campus ideological and political public opinion monitoring. We found an effective transmission method, explored the network energy saving, dig deep into the transmission function of the signal system, and reduced the interference and mutual influence of various transmission accessories. In the application of the Internet of Things, the network public opinion is remotely monitored, the campus public opinion information is mastered, and its dissemination and development orientation are controlled. The exploration and research of online public opinion monitoring are in line with the goal of smart campus, and its theoretical development is constantly enriched.

Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data

AbstractAs COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.

The Media Public Opinion Analysis on the Implementation of “Double Reduction” Policy in Education Based on Big Data

Monitoring the internet media opinion information quickly triggered by the implementation of the “double reduction” policy, and identifying the hot spots, tendencies, communication trends, and characteristics of media opinion are of great significance to government departments to grasp the dynamics of media opinion in time and assist them in decision-making. This paper uses the web crawler to dynamically collect the internet media opinion big data related to the “double reduction” of main media and utilizes text analysis and data mining technologies to dynamically monitor the hot spots, topic tendencies, and communication characteristics of media opinion. Taking the internet media opinion data on the implementation of the “double reduction” policy from January 1, 2021 to November 15, 2021 as the sample, the paper analyzes the media opinion of the implementation of the “double reduction” policy. The high incidence period of media opinion is consistent with the opening of primary and secondary schools, holidays, and the introduction of relevant policies. The topics of internet media opinion are highly targeted and cover a wide range. The media focuses on the implementation measures, new problems, new phenomena, effects, and evaluation of the “double reduction” policy in education. Strong provinces of education, large provinces of education, and difficult provinces of education are the main areas for media public opinion monitoring. The media opinion is in the fermentation stage, and there is no sign of heat reduction. The propagation of media opinion is in line with the theoretical characteristics of information propagation life cycle. This paper only takes the internet media opinion of the main network media as the monitoring object. Whether it is suitable for the media opinion of the whole network media needs to be further tested.

Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph

The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about emergencies. Considering the relevance and transmissibility of the same event in different social networks, this paper takes the COVID-19 outbreak as the background and selects the platforms Weibo and TikTok as the research objects. In this paper, first, we use the transfer learning model to apply the knowledge obtained in the source domain of Weibo to the target domain of TikTok. From the perspective of text information, we propose an improved TC-LDA model to measure the similarity between the two domains, including temporal similarity and conceptual similarity, which effectively improves the learning effect of instance transfer and makes up for the problem of insufficient sample data in the target domain. Then, based on the results of transfer learning, we use the improved single-pass incremental clustering algorithm to discover and filter popular topics in streaming data of social networks. Finally, we build a topic knowledge graph using the Neo4j graph database and conduct experiments to predict the evolution of popular topics in new emergencies. Our research results can provide a reference for public opinion monitoring and early warning of emergencies in government departments.

The Impact of a Knowledge Discovery-Based Psychoanalytic Intervention in the Treatment of Tuberculosis in University Students with Different Doses of Isoniazid.

Tuberculosis (TB) is an infectious disease that poses a serious threat to the health of the population in China, and TB outbreaks in universities have aroused great concern in society. Psychological emotions have a large impact on the academic lives of university students, and nowadays it is not only labour-intensive but also slow to monitor and analyse and deal with the psychology of university students' daily lives in a uniform manner. If psychological problems are not detected and given feedback in a timely manner, they can have a series of negative effects on the individual university student. In this paper, we apply the Bi-LSTM model and the CNN model neural network algorithm to learn the text data, and finally have 95.55% and 90.03% accuracy in the sentiment analysis experiment, respectively, which provides a feasible solution to solve the batch rapid analysis of the psychological changes reflected in the daily text of university students. Risk communication for TB emergencies should emphasize public participation, timely release of information about the epidemic, and good monitoring of public opinion.

Social Public Opinion Communication and Network Legal Management Based on Artificial Intelligence Cognitive Wireless Network

Public opinion supervision is an important part of the national security supervision system. In the Internet era, especially with the rapid development of mobile social media, the position of online public opinion supervision in public opinion supervision is becoming more and more prominent. Internet public opinion supervision has a wide range of subjects, diverse objects, multiple channels, instant and efficient communication speed, and two-way interaction and has its distinctive features, but at the same time there are phenomena such as online rumors, invasion of others' privacy, and venting private anger. The existence of these problems requires strengthening the legal system of public opinion supervision, enhancing the network literacy of Internet users, government management departments, website operators, and other subjects of online public opinion supervision, continuously improving the technical level of online public opinion monitoring, and public opinion guidance ability of network supervision departments, and strengthening the regulation of online public opinion supervision through the improvement and improvement of legal system and coordinated promotion of cyberspace governance. Meanwhile, mobile Internet is the soil for social opinion dissemination. With the development of technology, cognitive wireless network is becoming an important infrastructure in the mobile Internet. Therefore, this paper studies social opinion dissemination and cyber law management in cognitive wireless networks and proposes a cognitive engine model based on artificial intelligence algorithms for cognitive wireless networks for enhancing opinion dissemination and law management. The extended experiments demonstrate the advanced performance and effectiveness of the proposed method in this paper and provide a new technical approach for enhancing public opinion dissemination and legal management.

Reduction in the prevalence of tobacco use accompanying legislative changes in tobacco policy in Poland in the years 2010-2019.

Tobacco use is one of the most serious public health problems. Each year, it contributes to preventable disability and death of 8 million people worldwide. The aim of the study was to determine the change in the prevalence of tobacco and e-cigarettes use among Polish adults in the years 2010-2019 and the potential impact of legislative interventions on tobacco consumption in Poland. The research was based on an analysis of secondary data obtained from a cross-sectional study as part of the public opinion monitoring in Poland. The study was carried out in 2 editions (2010 and 2019) on representative samples of approximately 1000 Polish residents >18 years of age. The proportion of traditional cigarette smokers decreased from 30.4% to 26.0% in the years 2010-2019 (p < 0.05). In the male group decreased from 40.3% to 31.0% (p < 0.001); among females, the values remained at the same level (21.3%). The greatest change in the percentage of current smokers was recorded in cities with ≥500 000 inhabitants (from 30.3% to 17.1%) and <100 000 inhabitants (from 31.5% to 24.6%) (p < 0.05). The total support for the new legal regulations increased from 73.9% to 89.8% (p < 0.001). The use of e-cigarettes in 2019 declared only 1.9%. The percentage of adult traditional cigarette smokers decreased significantly between 2010 and 2019 (mainly among men). Among women, the percentage of cigarette smokers remained the same. The most significant declines in cigarette smoking were also observed among residents of the largest cities (≥500 000 inhabitants). During the same period, there was a further increase in the acceptance of legal restrictions on smoking cigarettes in public places. E-cigarette use among adults is a niche phenomenon in Poland, but it is much more prevalent among women than men. Int J Occup Med Environ Health. 2022;35(4):393-405.

Multichannel Fusion Based on modified CNN for Image Emotion Recognition

&lt;p&gt;Social media networks are an integral part of people’s daily life. Users share images and texts to express their emotions and opinions. Analyzing the image and text content published by these users can help understand and predict user behavior, so as to carry out marketing, public opinion monitoring and personalized recommendation. Weibo, Wechat and other social media are important ways of self-expression. Images are more intuitive than text. Therefore, more scholars begin to pay attention to the research of image emotion analysis. At present, image emotion analysis methods pay seldom attention to the influence of saliency object and face on image emotion expression. Therefore, we propose a multichannel fusion method based on modified CNN for image emotion recognition. Firstly, saliency target and face target region are detected in the whole image. Then feature pyramid is used to improve CNN to recognize saliency target emotion. Weighted loss CNN emotion recognition is constructed on multi-layer supervision module. Finally, the saliency target emotion, face target emotion and the directly recognized emotion on the whole image are fused to get the final result of emotion classification. Experimental results show that the proposed method can improve the accuracy of image emotion recognition.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Real Time Big Data Sentiment Analysis and Classification of Facebook

Many peoples use Facebook to connect and share their views on various issues, with the majority of user-generated content consisting of textual information. Since there is so much actual data from people who are posting messages on their situation in real time thoughts on a range of subjects in everyday life, the collection and analysis of these data, which may well be helpful for political decision or public opinion monitoring, is a worthwhile research project. Therefore, in this paper doing to analyze for public text post on Facebook stream in real time through environment Hadoop ecosystem by using apache spark with NLTK python. The post or feeds are gathered form the Facebook API in real time the data stored database used Apache spark to quick query processing the text partitions in each data nodes (machine). Also used Amazon cloud based Hadoop cluster ecosystem into processing of huge data and eliminate on-site hardware, IT support, and other operational difficulties and installation configuration Hadoop such as Hadoop distribution file system and Apache spark. By using the principle of decision dictionary, emotion analysis is used as positive, negative, or neutral and execution two algorithms in machine learning (naive bias &amp; support vector machine) to build model predict the outcome demonstrates a high level of precision in sentiment analysis.

Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process's complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users' interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making.

Multi-level Attention Map Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) is a very challenging task due to its complex and complementary interactions between multiple modalities, which can be widely applied into areas of product marketing, public opinion monitoring, and so on. However, previous works directly utilized the features extracted from multimodal data, in which the noise reduction within and among multiple modalities has been largely ignored before multimodal fusion. This paper proposes a multi-level attention map network (MAMN) to filter noise before multimodal fusion and capture the consistent and heterogeneous correlations among multi-granularity features for multimodal sentiment analysis. Architecturally, MAMN is comprised of three modules: multi-granularity feature extraction module, multi-level attention map generation module, and attention map fusion module. The first module is designed to sufficiently extract multi-granularity features from multimodal data. The second module is constructed to filter noise and enhance the representation ability for multi-granularity features before multimodal fusion. And the third module is built to extensibly mine the interactions among multi-level attention maps by the proposed extensible co-attention fusion method. Extensive experimental results on three public datasets show the proposed model is significantly superior to the state-of-the-art methods, and demonstrate its effectiveness on two tasks of document-based and aspect-based MSA tasks.

Does the COVID-19 Vaccine Still Work That "Most of the Confirmed Cases Had Been Vaccinated"? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing.

In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine’s effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine’s effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public’s cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues.

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments

Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed.

Prediction of network public opinion based on bald eagle algorithm optimized radial basis function neural network

PurposeIn the new era of highly developed Internet information, the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approachAiming at the complex and nonlinear characteristics of the network public opinion, considering the accuracy and stability of the applicable model, a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network (BES-RBF) is proposed. Empirical research is conducted with Baidu indexes such as “COVID-19”, “Winter Olympic Games”, “The 100th Anniversary of the Founding of the Party” and “Aerospace” as samples of network public opinion.FindingsThe experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information, has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/valueA method for optimizing the central value, weight, width and other parameters of the radial basis function neural network with the bald eagle algorithm is given, and it is applied to network public opinion trend prediction. The example verifies that the prediction algorithm has higher accuracy and better stability.

MBRep: Motif-based representation learning in heterogeneous networks

In recent years, there has been a surge of interest in applying machine learning to graphs and networks that already exist in the world around us. The approach has been successfully used for domains as diverse as traffic management, e-commerce recommendation and public opinion monitoring. A critical aspect of representation learning for applied machine learning is feature engineering. Deep learning-based research in representation learning has developed methods for automatically learning a large number of potentially correlated features from original networks. However, most of these methods cannot be applied to heterogeneous networks, which are true expressions of the real-world. This is because they do not adequately capture the structure and semantics of different types of nodes in heterogeneous networks and the links between them. They also struggle to represent higher-order heterogeneous patterns of connection. This paper proposes a generalized motif-based higher-order representation learning method, MBRep, that learns triangle motif embedding in a network, on the basis of which it can obtain the embedding and representation of nodes in a heterogeneous network. Statistically, significant motif structures are extracted from the original heterogeneous network and combined with the heterogeneity of the nodes. A weight-biased random walk is then applied to the motif level higher-order network, using a SkipGram model to embed the motifs. After this, the embedding of the original network nodes is calculated using weighted averages and feature alignment. This can then be used for link prediction. We confirmed the effectiveness of MBRep by comparing its AUC and MRR performance with other state-of-the-art methods on three real-world datasets. Its adaptability was also validated by conducting a cold-start test.

Police legitimacy

The subject of the article is assessment of public confidence in the police and the legality of its activities by the opinion of the Russian population. The purpose of the research is to identify the reasons that affect the assessment of the police legitimacy, its perception by the population. The authors also dare to identify the role of "problem areas" of police activity (related to different violations of the law) in people assessment.The methodology of the research includes: sociological survey among Russians, comparative criminological, statistical, hermeneutic methods, expert assessments.The main result, scope of applications. The central link of the police legitimacy is public trust, recognition, leading to the cooperation between the people and the authorities. Legitimacy assessments are formed based on the study of public opinion – the official monitoring procedure. The website of the Russian Ministry of Internal Affairs provides information on the results of monitoring public opinion about the activities of the police, but an analysis of its content allows us to note its superficiality and unconvincing optimism. This is obviously affected by the shortcomings of the departmental legal regulation of the process of monitoring public opinion about the activities of the police, which, judging by the questionnaire questions (there are only three of them), is rather imitative in nature. We believe that the official monitoring of public opinion on the activities of the police, at a minimum, should be supplemented by the results of independent sociological studies aimed at establishing "problem areas" of police activity related to violations of the law, violence, ill-treatment, provocations, inducing someone, directly or indirectly, to commit illegal actions, other official abuses. Otherwise, the regulatory requirements for the activities of the police, which establish the foundations of its legitimacy, cannot be fully implemented. The survey of 362 citizens conducted by the authors allows to state that, with a fairly high assessment of the activities of the Russian police, respondents pay attention to the existence of such problems as: the lack of transparency of police activities and the presence of formal and informal means of evading the legality of police activity, the tolerance of justice authorities to police lawlessness, intoxication with power by police officers, the impact of police brutality on social and legal despair, violation of the law in the name of achieving its falsely understood goals. The extreme case of perverted official zeal is the execution of illegal orders. There are quite widespread ideas in public opinion that entrepreneurs often become victims of extortion by the police, that the police take bribes from medium and small businesses, that you can buy off police officers. At the same time, the results of the study indicate that a significant part of the population is determined to cooperate with the police.Conclusions. The public demand for the police is relevant, the honor and main meaning of which is to protect the interests of all people and serve justice. People need such policemen who will demonstrate not strength and power, but simple human complicity. This is the main secret of the legitimacy of the police. Another secret lies in the orientation of personnel policy towards professionals in the police: their selection, training and support in difficult situations.

Национальный проект «Демография» и перспективы демографического роста глазами экспертного сообщества

The processes of strengthening the country’s security are inextricably linked with ensuring demographic security, preserving and supporting the population, improving the quality of life, and this direction of state and civil efforts is one of the most important for modern Russia. Within the framework of the concept of overcoming the most pressing demographic threats – depopulation and unregulated migration processes, in addition to the pro-natalist policy of the state, the creation of economic conditions for improving the quality of life in general, the most popular and at the same time controversial tool for solving demographic problems is the migration policy aimed at attracting migrants from foreign countries. countries that must solve both the demographic and economic problems of the state. The article presents the results of expert polls of sociological research in 2019 and 2021. “Monitoring of public opinion of the population regarding current socio-cultural threats”, “Image of the future of Russia” conducted by the Center for Sociological Research of the Russian State Humanitarian University using a similar method, which analyzes the public opinion of experts on trends in Russia’s demographic development and possible sociocultural threats associated with them

Network Public Opinion Monitoring Based on MapReduce

Public opinion information on the network is abundantly decentralized in Internet, these false ones, easily misleading, not legally binding, it is difficult to deal with. Quickly master information is a prerequisite to deal with the network of public opinion, clustering algorithm in data mining plays an important role in in the collection of information statistics. Disadvantages of classical clustering methods are high resources consumption, low efficiency, high time and space complexity, and difficult to deal with large-scale data processing in massive network. To the network public opinion text as the research object, in-depth study of summarizing the network public opinion monitoring technology based on distributed MapReduce, including parallel technology, improved clustering algorithm, relational database and distributed database construction, which is in order to improve the efficiency of information processing, to reduce the pin.