Characteristics Of Public Opinion Research Articles

Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art

The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.

Opini Publik Followers Instagram Volix Media sebagai Media Pemenuhan Kebutuhan Informasi Publik

This article aims to evaluate public opinion, specifically the followers of Instagram Volix Media, regarding the extent to which the information presented meets their needs. The issue under scrutiny pertains to Instagram Volix Media, which has been providing diverse information about Indonesian pop culture, music, film, artists, news, art, and little-known culture since 2021. To approach this issue, Cutlip & Center's theory on the characteristics of public opinion is employed, encompassing five dimensions: direction, intensity, stability, informational support, and social support. Data were collected by distributing questionnaires to 100 respondents who are followers of Instagram Volix Media. The data were analyzed using a quantitative approach and descriptive methods.The results indicate that the dimension of informational support received the highest average score, suggesting that respondents have more information on the issues presented. However, the dimension of social support received the lowest average score, indicating a lack of perceived support from others regarding the opinions expressed. This article concludes that Instagram Volix Media exhibits a positive direction of opinion, with the majority of respondents agreeing that the information presented meets the needs of public information.

Research on online public opinion in the investigation of the “7–20” extraordinary rainstorm and flooding disaster in Zhengzhou, China

Sorting out the changing characteristics of online public opinion triggered by a series of events in the investigation and assessment of major natural disasters is of great practical significance for optimizing the work of disaster investigation and assessment, governing the ecology of online public opinion, and enhancing the effect of comprehensive disaster reduction. In this paper, we collected relevant comments from several official media accounts, such as People's Daily, and evaluated their emotional color using a sentiment analysis method based on the BERT fine-tuning model. Furthermore, keyword co-occurrence semantic network theme analysis is conducted for texts presenting negative emotional overtones to assess the changes in public opinion hotspots. The impact of the relevant online public opinion characteristics and the release of the disaster investigation report on them was assessed in the context of the investigation report itself. Based on sorting out the characteristics of online public opinion on several related topics, targeted public opinion governance initiatives and relevant suggestions for improving the disaster investigation system are proposed. This paper is of positive significance for studying disaster public opinion and improving the effectiveness of emergency management of rainstorms and flooding disasters.

Retention or Abolition of the Death Penalty and Public Opinion on the Death Penalty

The discussion on the death penaltys retention or repeal has been going on for a long time, and there are different views on the retention and elimination of the death punishment. Despite the international trend toward abolition, certain countries, like China, Japan, and the United States, continue to use the death sentence, and all three countries have conducted public opinion surveys on the abolition of the death sentence, and public opinion has a certain influence on the death penalty reform. This article will compare the views of Beccaria and Bentham, the representatives of abolitionism, and Kant and Hegel, the representatives of capital punishment retention, review the outcomes of popular opinion surveys on capital punishment in China, the United States, and Japan, analyze the characteristics of public opinion, and discuss the interaction between capital punishment retention and abolition and public opinion. The study about the value of public views in terms of the death sentence reform is of reference significance to the issue of whether the death penalty should be retained or abolished.

Общественное мнение и публичное пространство: проблема смысловой взаимосвязи в рамках системно-коммуникативного подхода

In this article, the author investigates two social phenomena, namely, public opinion and public space, within the framework of the systems-communicative approach. The study is relevant due to the fact that today, both in Russia and beyond, the topic of public opinion, publicity and mass beliefs has received great attention from various spheres of public life, in particular, the political sphere, which can automatically generate research interest within the academic environment. The theoretical basis for this paper is Niklas Luhmann’s general social theory. The purpose of the study is to explicate the special characteristics of public opinion as a truly unique communication space necessary for the political system to reproduce itself. The author considers the problem of the interpretation of public opinion and its characteristics as a communicative phenomenon and consistently outlines the construct of Luhmann’s theoretical positions. Recognizing the special role of the media as an actor that gives a distinct shape to public opinion, the author demonstrates that the popular view of mass media as a manipulator of mass beliefs is often exaggerated and even prescribed: being guided by the desire to create a sensation, mass media can offer or even impose an agenda, but cannot force its full acceptance. It is postulated here that not only does the news seek its reader, but the reader seeks his/her news. Based on these positions, the author concludes that, within the framework of the political system, public opinion is a communicative tool for making universally binding decisions, rather than a sum of individual views, thus demonstrating the mythological content of the concepts of democracy, elections, and popular will.

Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events.

An Analysis of the Evolution of Online Public Opinion on Public Health Emergencies by Combining CNN-BiLSTM + Attention and LDA

In this paper, the Foxconn epidemic event in Zhengzhou was taken as an example to analyze the evolution of online public opinion on public health emergencies. In order to improve the performance of online public opinion analysis, based on the life cycle theory and LDA theory, the emotional changes of Internet users in four stages of the Foxconn incident centered on the evolution of inscription were divided. The emotions of netizen speech at different stages are analyzed based on CNN-BiLSTM + Attention model, which uses Convolutional Neural Network (CNN) to extract local features. Bi-directional Long Short-Term Memory (BiLSTM) is used to efficiently extract contextual semantic features and long distance dependencies, and then combined with attention mechanism to add emotional features. Finally, Softmax classifier realizes text emotion prediction. The experimental results show that: compared with TextCNN, BiLSTM, BiLSTM + Attenion, CNN-BiLSTM model, the emotion classification model has better effects in the accuracy rate, accuracy rate, recall rate and F value. By analyzing the emotional distribution and evolution trend of public opinion under “text topic”, the paper accurately deconstructs the development characteristics of public opinion in public health emergencies, in order to provide reference for relevant departments to deal with public opinion in public health emergencies.

Research on the co-evolution of temporal networks structure and public opinion propagation

Under the new media environment, social platforms, as the carrier of information propagation, have shown a drastic change in their form and structure, endowing public opinion with unique propagation characteristics. In view of this, considering the dynamic changes of online social network (OSN) structure, this article intends to analyse the spreading mechanism of public opinion in temporal networks and improve the applicability of public opinion governance strategies. Combing the changes of OSN topology with the classical susceptible–infected–recovered (SIR) dynamics model, we constructed a co-evolution model of temporal networks structure and public opinion propagation, and the propagation threshold of public opinion was derived with the help of Markov process. Then, the propagation characteristics of public opinion in temporal networks and their co-evolution process under different factors were discussed through simulation experiments. The results show that the propagation of public opinion in temporal networks has faster speed and wider scope compared with that in static networks; netizens’ social activity has a phased impact on the evolution process of public opinion and with its significant heterogeneity, the propagation of public opinion is gradually suppressed; compared with [Formula: see text], the evolution process of public opinion in temporal networks is more sensitive to the state change of public opinion [Formula: see text]. Our research can further enrich the theoretical research system of network science and information science and also provide a certain decision-making reference for the regulators to reasonably govern the propagation of public opinion in social platforms.

Research on public emotional polarization and public opinion evolution of OTC and learning during the COVID-19 epidemic: taking the topic of OTC on Zhihu as an example

PurposeThe outbreak and continuation of COVID-19 have spawned the transformation of traditional teaching models to a certain extent. The Chinese Ministry of Education’s guidance on “keep learning and teaching during class suspension” has made OTC and learning (OTC) become routinized, and the public’s emotional attitudes toward OTC have also evolved over time. The purpose of this study is to segment the emotional text data and introduce it into the topic model to reveal the evolution process and stage characteristics of public emotional polarity and public opinion of OTC topics during public health emergencies in the context of social media participation. The research has important guiding significance for the development of OTC and can influence and improve the efficiency and effect of OTC to a certain extent. The analysis of online public opinion can provide suggestions for the government and media to guide the trend of public opinion and optimize the OTC model.Design/methodology/approachThis paper takes the topic of “OTC” on Zhihu during the COVID-19 epidemic as an example, combined with the characteristics of public opinion changes, chooses Boson emotional dictionary and time series analysis method to build an OTC network public opinion theme evolution analysis framework that integrates emotional analysis and topic mining. Finally, an empirical analysis of the dynamic evolution of the communication network for each stage of the life cycle of a specific topic is realized.FindingsThis paper draws the following conclusions: (1) Through the emotional value table and the change trend chart of the number of comments, the analysis found that the number of positive comments is greater than the number of negative comments, which can be inferred that the public gradually accepts “OTC” and presents a positive emotional state. (2) By observing the changing trend of the average daily emotional value of the public, it is found that the overall emotional value shows a stable development trend after a large fluctuation. From the actual emotional value and the fitted emotional value curve, it can be seen that the overall curve fit is good, so ARIMA (12, 1, 6) can accurately predict the dynamic trend of the daily average emotional value in this paper. Therefore, based on the above-mentioned public opinion, emotional analysis research, relevant countermeasures and suggestions are put forward, which is conducive to guiding the development direction of public opinion in a positive way.Originality/valueTaking the topic of “OTC” in Zhihu as an example, this paper combines Boson emotional dictionary and time series to conduct a series of research analyses. Boson emotional dictionary can analyze the public’s emotional tendency, and time series can well analyze the intrinsic structure and complex features of the data to predict the future values. The combination of the two research methods allows for an adequate and unique study of public emotional polarization and the evolution of public opinion.

Cross-Dimension Mining Model of Public Opinion Data in Online Education Based on Fuzzy Association Rules

The multi-dimensional characteristics of public opinion in online education lead to the difficulty of data cross-dimensional mining. To solve this problem, this paper designs a cross-dimensional data mining model of public opinion in online education based on fuzzy association rules. Based on the public opinion subject, object, and ontology to analyze the characteristics of public opinion in online education, Yaahp software is used to calculate the influence factor weight of public opinion in online education. According to the weight analysis results, the relationship between the dimensions of various public opinion data is clarified by using data semantic association. This paper introduces the fuzzy set theory into the database and uses crawlers to obtain public opinion data and stores them in the database, to complete the data preprocessing through distributed text preprocessing, feature selection distributed computing, and text vectorization distributed computing. Taking the cloud computing platform as the core, the cross-dimension mining model of public opinion in online education data is constructed according to the dimension correlation analysis and preprocessing results. The simulation results show that the model has the advantages of wide range, fast speed, and high accuracy, and can provide data support for education reform.

A multidimensional network link prediction algorithm and its application for predicting social relationships

In the "We the Media" era, the rules for forming social users' following relationships are complex. Links generated between two social user nodes are influenced by not only the structural information of their social network nodes but also the users’ occupational environments, interests in opinions, topics, social psychology, etc. In the existing studies of link prediction in complex networks, predicting the possibility of link generation between two nodes that have not yet generated edges in a complex network is calculated mainly from the known network nodes and structural information. Such studies, in which the main predictors are the structural similarities among social nodes or user location nodes, cannot fully explore and utilize the social network node users’ public opinion characteristics. To quantitatively identify the influence of different dimensions of public opinion factors on predicting links between social user nodes, we present a study on the prediction of social network links in "We the Media" networks. Starting with the characteristics of the elements found in public opinions on “We the Media” networks, in which public opinions are multidimensional and multilayered and possess multiple attributes, we built a multidimensional network model oriented towards the topology of public opinions on “We the Media” networks. Combined with an analysis of the driving factors in the formation of user-node relationships in social networks, we designed a prediction algorithm that works on multidimensional network links. Furthermore, we conducted an empirical analysis of social relationship prediction, whose effectiveness has also been compared with baseline methods such as the Common-Neighbourhood-Driven model, the Jaccard index, and the SimRank method. We chose the area under the curve (AUC) as the indicator of link prediction and evaluation using “We the Media” public opinion data from Weibo.com. The research findings of this paper can be summarized as follows: (1) The effectiveness of the multidimensional network link prediction algorithm is significantly higher than those of the baseline methods. (2) The prediction algorithm presented in this paper works on multidimensional network links and can evaluate the effects of different dimensions of public opinion factors on the prediction of user-node links in social networks. (3) The element of occupational environment improves the accuracy much more than the element of user interest in opinions and topics when predicting user-nodes’ links, while the element of social psychology reduces the accuracy.

Discussion on health protection and disinfection strategies for the prevention and control of COVID-19 based on public opinion

This paper summarizes the development trend and characteristics of public opinion on health protection and disinfection strategies in the COVID-19 epidemic. The experience and deficiency of the strategies are discussed from the perspective of public opinion, and suggestions on how health protection and disinfection can help prevent and control infectious diseases are also put forward: to strengthen health protection and disinfection in key places and units; to evaluate health protection and disinfection effects as well as the transmission mechanism of virus in the environment; to establish a professional health protection and disinfection emergency science popularization mechanism and information release channel; to speed up the formulation and revision of health protection and disinfection standards related to the epidemic.

Легітимаційний потенціал сучасної політичної маніпуляції

The article examines the loss in modern realities of public opinion characteristics of integration, structural unity, ideological motivation. In the digital age, the concept of legitimacy acquires semantic dispersion, acquires connotations of curiosity, attractiveness, «media historicity», manufacturability, and so on. It is proved that in the conditions of a continuous environment of political manipulation no power team can deviate from such means in order not to lose competitive advantage. However, there is a question about measuring the effectiveness of political manipulation, its moral dimension and the social responsibility of manipulators. Reasonably, the democratic conditions of political competition determine the need for a certain style of image behavior, which comes from the ruling team and is aimed at maintaining a stable model of society. It is established that the political and technological means of implementing legitimation strategies involve the formation of a consistent image of power. In real conditions of political openness, this is subject to manipulation. The positive significance of normative-legal changes is revealed, which supports the image of the government as capable of controlled innovations. In such circumstances, political manipulation is a factor in giving positive meaning to the actions of existing centers of power. In the context of democratic transformation, the legitimation of power requires broad social reforms without cooperation with leading social groups in order to increase their welfare. It is noted that modern «manipulative ideology» is an eclecticism from different ideological traditions, aimed at convincing the broadest possible sections of citizens. It was found that each new regime in the conditions of political transformations forms a situation of a new start of democratic reforms (in particular, as it was in Ukraine in 2014 and 2019). It has been found that the means of misinformation, disorientation, the practice of silence, «post-truth» and «half-truth» are not just tools for persuading and creating a picture of the world. They embody the desire of the ruling group to gain control over society on a rational and technocratic basis. It is proved that the practices of persuasion, deception, use of individuals and groups as objects of influence are inferior to the means of displacing the most effective forms of media presentation of political opponents. Thus, the main means of manipulative legitimation of political power in the information-digital age and hyperbolization of political competition is to create an active context of positive and negative messages about power and regulate their balance.

Analysis of microblog public opinion characteristics on traditional Chinese medicine against COVID-19 based on deep learning

Analysis of microblog public opinion characteristics on traditional Chinese medicine against COVID-19 based on deep learning

Public Opinion Guidance with New Government Media In the Age of Big Data

The Internet has changed the way of people’s life, meanwhile, the emergence of new media is changing people’s habit of reading news and getting information. Recently, in order to take use of the new media platform to guide the network public opinion, The government gradually increased the construction of new government media. This paper aim to study the new characteristics of public opinion in new media age, analyzes the problems of the new government media in the public opinion guiding, and also put forward some optimization strategies.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

The simultaneous growth of textual data and the advancements within Text Analytics enables organisations to exploit this kind of unstructured data, and tap into previously hidden knowledge. However, the utilisation of this valuable resource is still insufficiently unveiled in terms of transport policy decision-making. This research aims to further examine the potential of textual big data analytics in transportation through a real-life case study. The case study, framed together with the Danish Road Directorate or Vejdirektoratet, was designed to assess public opinion towards the adoption of driverless cars in Denmark. Traditionally, the opinion of the public has often been captured by means of surveys for the problem owner. Our study provides demonstrations in which opinion towards the adoption of driverless cars is examined through the analysis of newspaper articles and tweets using topic modelling, document classification, and sentiment analysis. In this way, the research attends to the collective as well as individualised characteristics of public opinion. The analyses establish that Text Analytics may be used as a complement to surveys, in order to extract additional knowledge which may not be captured through the use of surveys. In this regard, the Danish Road Directorate could find the usefulness while understanding the barriers in the results generated from our study, for supplementing their future data collection strategies. However there are also some methodological limitations that need to be addressed before a broader adoption of textual big data analytics for transport policy decision-making may take place.

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

In recent years, with the rapid development and wide application of the Internet, it has become the main place for the generation and dissemination of public opinion. To grasp the information of network public opinion in a timely and comprehensive way can not only effectively prevent sudden network malignant events but also provide a reference for the scientific and democratic decision-making of government departments. Therefore, in view of the practical application needs, this article studies the emotional characteristics and the evolution of public opinion over time based on the emotional feature words of network public opinion participants. Firstly, the positive and negative emotional lexicon of HowNet emotional dictionary is used, and the commonly used emotional lexicon and expression symbols are added to the lexicon. At the same time, the polarity annotation method of Chinese emotional lexicon ontology is used to construct the emotional lexicon of this article. Secondly, considering other emotional polarity characteristics in the dictionary, an emotional tendency analysis model is proposed. In this article, emotional analysis is applied to the evolution analysis of network public opinion, and the change of network public opinion characteristics with time series is obtained. The simulation results show that the emotional dictionary constructed in this article and the proposed model of emotional orientation analysis can effectively analyze the emotional characteristics of network public opinion participants and apply emotional analysis to the evolution analysis of network public opinion, which can get the change of emotional characteristics of public opinion participants with time series.

Monitoring the internet public opinion of mountain flood disaster using big data analysis

The mountain torrent disasters have caused severe damage and loss of life and property to the society and the economy, which attract the public’s attention. Timely monitoring the characteristics and evolution of internet public opinion in mountain torrents is of significant impact on the emergency response and management. The primary objective of the paper is to analyze the time and spatial characteristics, attention key points, emotional trend and participants of the public opinion in mountain torrent disasters using big data and statistical analysis. The current situation is investigated and a prediction method using correlation analysis is proposed, which provides reference for decision makers to formulate response plans according to public opinion from internet. The data mining method is used to obtain relevant data based on microblog API interface, and the data is processed by statistical methods. Combined with the content analysis method, the characteristics of public opinion in mountain torrents are comprehensively analyzed, and the hotspots of public concern and the trends of public opinion are obtained. The results show that the eruption time of the public opinion coincides with the disaster occurrence time. From spatial characteristics analysis, the flood disasters in China mainly occur in the North China Plain, the middle and lower reaches of the Yangtze River, the Pearl River Basin, the Sichuan Basin, and the Northeast Plain. The public opinion attention degrees are highly positive correlated with the occurrence of mountain torrent disasters, and the correlation coefficient is 0.96. Citizens not only pay attention to the disaster situation and rescue work, but also focus on the improvement of the disaster prevention work. Public emotional trend is mainly based on expressing hope for a safe life, pity and sympathy for the victims, panic about the severity of the disaster, objective evaluation of the situation, as well as admiration and concern for rescuers, and suggestions on early warning education. As an opinion leader, People Daily has great influence on the internet public opinion. At different stages, participants should work together to maintain a healthy public opinion environment.

Оценка гражданами состояния правопорядка и уровня безопасности в мегаполисе: результаты криминологического мониторинга

Objective: on the basis of sociological research, to analyze, study and disclose the essential characteristics of public opinion of Nizhny Novgorod citizens on the state of law and order and security in the metropolis; to formulate recommendations to employees of the internal affairs bodies engaged in providing public order during cultural and sports events, in particular, World Cup 2018.Methods: general scientific (analysis, system-structural approach to the analysis of the study object, comparative-legal method), as well as logical and specific methods (survey method - a survey of Nizhny Novgorod residents).Results: the characteristics of public opinion of Nizhny Novgorod citizens on the level of law and order and security in the metropolis are identified and classified; the factors that provide the feeling of the greatest security and safety, as well as the attitude to World Cup 2018 in different categories of the population are determined; the typology of population groups is carried out depending on the attitude to conducting other large-scale events in the city.Scientific novelty: the article, based on the analysis of public opinion of Nizhny Novgorod residents, for the first time identifies and systematizes the main characteristics, including the problem ones, of the state of law and order and security in the Nizhniy Novgorod metropolis, proposals and practical recommendations were formulated on increasing the efficiency of activity of the regional internal affairs bodies during mass cultural, international, sports events: a) account of the particular location of the settlement, which will host a mass event; b) the internal affairs bodies’ administration should constantly be in a dialogue with the district administrations of the municipality on the issues of crime prevention; c) the citizens should be legally informed about the peculiarities of behavior during mass events; d) increased requirements for the appearance, culture of communication and competence of police officers during mass events; e) strengthening control over visitors from the region or the nearest regions and their stay in the central part of the city.Practical significance: the study results can be used to adjust the work of the territorial bodies of the Russian Ministry of Internal Affairs to optimize and improve activities to ensure law and order and security as a whole, and in the case of the organization of cultural, sports, including international, events. The results of the study can be used in the organization of work to improve the efficiency of the regional internal affairs bodies, to ensure the planning of operational activities and the development of appropriate management decisions in law enforcement.

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.