Public Opinion Data Research Articles

Analysis of public opinion on employment issues using a combined approach: a case study in China.

To analyze the public opinion related to the employment situation, a combined approach is proposed to study the valuable ideas from social media. Firstly, the popularity of public opinion was analyzed according to the time series from a statistical point of view. Secondly, the feature extraction was carried out on the public opinion information, and the thematic analysis of the employment environment was carried out based on the Latent Dirichlet Allocation model. Thirdly, the Bert model was used to analyze the sentiment classification and trend of the employment-related public opinion data. Finally, the employment public opinion texts in different regions were studied based on the spatial sequence popularity analysis, keyword difference analysis. A case study in China is conducted to verify the effectiveness of proposed combined approach. Results shown that the popularity of employment public opinion reached the highest level in March 2022. Public opinions towards employment situation arenegative. There is a specific relationship between the popularity of employment public opinion in different provinces.

Stance Analysis of Policies Related to Emission Test Obligations using Twitter Social Media Data

Social media is currently widely used to disseminate various kinds of information, whether expressing feelings, or opinions. Public opinion is no exception regarding government policies and the implementation of emission tests, which describe the conditions that exist in society. Information on public opinion data obtained through social media in real time can assist the government in evaluating policies and improving the quality of currently implemented policies, particularly evaluating the implementation of emission tests on motorized vehicles. In this research, the application of stance analysis is used to evaluate emission test policies based on public opinion.In addition, this research aims to combine several machine learning methods and feature extraction methods to find the best combination based on accuracy, training time, and prediction time based on emission test policies. The best model based on the level of accuracy is a combination of Decision Tree and BERT, which reaches a value of 66%. Meanwhile, based on training time, the model that has the advantage is the Ridge Classifier with fasttext text representation. Based on prediction time, there are 3 combination models, namely Decision Tree with word2vec, SVM with Word2Vec, and Logistic Regression with fasttext text representation.

Evaluation of Perceived Environmental Risks of Residents' Life under the Perspective of Environmental Public Opinion

Population health, social development and the environment are important elements of sustainable development. This article uses the China People's Daily message board to collect environmental public opinion data, visualizes the public opinion hotspots of environmental based on word frequency statistics, and applies the Latent Dirichlet Allocations (LDA) topic model to analyze the spatial distribution of environmental risk perception dimensions. The conclusions are as follows: (1) in terms of the hotspots of environmental public opinion, the most frequent occurrences are the living environment, interest demands and noise pollution. (2) There is heterogeneity in the topic of environmental risk perception. The perception of pollution source types has the highest weight in environmental risk perception in the eastern, central, and western regions. Specifically, the types of pollution sources in the eastern that receive the most attention are garbage and noise pollution. In addition to paying attention to noise pollution, the central and western also have a higher perceived weight on the polluters. Residents in northeast are most concerned about changes in residents' health and living environment quality. (3) From the perspective of environmental risk perception, health perception has the highest proportion in northeast, followed by the eastern, and finally the central and western. Proportion of perception of interest demands is highest in the central region, perception of residential environment quality is highest in the northeast region, perception of pollution source types is highest in the eastern, central, and western regions, and lowest in the northeast region. Finally, some practical and feasible policy recommendations were proposed for different regions.

A Temporal Variable-Scale Clustering Method on Feature Identification for Policy Public-Opinion Management

The development of various digital social network platforms has caused public opinion to play an increasingly important role in the policy making process. However, due to the fact that public opinion hotspots usually change rapidly (such as the phenomenon of public opinion inversion), both the behaviour feature and demand feature of netizens included in the public opinion often vary over time. Therefore, this paper focuses on the feature identification problem of public opinion simultaneously considering the multiple observation time intervals and key time points, in order to support the management of policy-focused online public opinion. According to the variable-scale data analysis theory, the temporal scale space model is established to describe candidate temporal observation scales, which are organized following the time points of relevant policy promulgation (policy time points). After proposing the multi-scale temporal data model, a temporal variable-scale clustering method (T-VSC) is put forward. Compared to the traditional numerical variable-scale clustering method, the proposed T-VSC enables to combine the subjective attention of decision-makers and objective timeliness of public opinion data together during the scale transformation process. The case study collects 48552 raw public opinion data on the double-reduction education policy from Sina Weibo platform during Jan 2023 to Nov 2023. Experimental results indicate that the proposed T-VSC method could divide netizens that participate in the dissemination of policy-focused public opinion into clusters with low behavioural granularity deviation on the satisfied observation time scales, and identify the differentiated demand feature of each netizen cluster at policy time points, which could be applied to build the timely and efficient digital public dialogue mechanism.

Polarização e ideologia: explorando a natureza contextual do compromisso democrático

ABSTRACT Introduction: Political polarization stands as a pivotal topic in contemporary Political Science. Existing literature indicates that ideological extremism, prevalent in polarized environments, fosters the erosion of democratic norms by political elites. This phenomenon has primarily been explored through the examination of isolated cases or limited comparisons among a handful of highly polarized countries. This article investigates the role of “guardians of democracy”, questioning whether specific ideological groups maintain their commitment to democratic principles even amidst profound polarization. Materials and methods: Our analysis draws on public opinion data from 57 countries, encompassing over 77,000 respondents, sourced from the most recent wave of the World Values Survey (2017-2020). We explore the interaction between contextual and individual factors, investigating the impact of national-level polarization on individuals with varying ideological orientations and how the government's dominant ideology shapes individual responses to political polarization. Results: There is a negative relationship between polarization and democratic legitimacy. However, the impact varies depending on the ideology of the voters and their alignment with the ideology of the ruling party. Discussion: Our analysis suggests that the interaction between contextual factors (polarization and government ideology) and individual factors (voter ideology) creates scenarios that foster different profiles of guardians of democracy. However, none of these scenarios involved right-wing individuals presenting themselves as such.

A Method for Tracing Big Data of Network Public Opinion Based on Data Mining Algorithms

A Method for Tracing Big Data of Network Public Opinion Based on Data Mining Algorithms

A method for tracing big data of network public opinion based on data mining algorithms

A method for tracing big data of network public opinion based on data mining algorithms

An improved model of the diffusion of Bass innovations for predicting changes in public opinion during the implementation of a narrative in social networks

During the writing of the article, the complex dynamics of forecasting public opinion based on the implementation of narratives in social networks were analyzed using Bass's innovation diffusion model. Recognizing the important role of narratives in the formation of collective views provides a basis for using Bass's innovation diffusion model to predict and understand changes in public attitudes during the introduction and implementation of narratives in social networks. On the basis of the above, the research task was set: improvement of the Bass innovation diffusion model for predicting changes in public opinion in social networks, which will directly contribute to the development of quality content by determined forces and means to create favorable conditions during the use of troops (forces) and in peacetime due to entering the coefficient of coverage of the target audience. During the writing of the article, theoretical methods were applied, namely, the analysis of research and publications on the topic of development in relation to the use of diffusion of innovations, which describes the process of how innovations are accepted by the population, the analysis of statistical data of public opinion, comparisons to identify development trends in the consumption of information in social networks, as well as synthesis to achieve the goal of the study. The article presents a mathematical apparatus that incorporates the Bass diffusion model into network behavior for modeling the spread of narratives and predicting changes in public opinion. For the first time, an approach to simulation modeling of the introduction and implementation of a narrative in a social network based on the Bass diffusion model was substantiated, which made it possible to investigate the consumer behavior of social network users under the influence of information and interpersonal communication in a social network. Integrating concepts from Bass's model, such as innovation adoption and imitative behavior, the proposed model aims to predict the dynamics of public attitudes in response to the spread of narratives.

Visualizing the Emergence of Political Homophobia: Anti-LGBTQ+ and Anti-Ukrainian Sentiment in Russian Public Opinion

On February 24, 2022, Russia invaded Ukraine, escalating a conflict that began in 2014 with the annexation of Crimea. Before the annexation, Russia’s anti-gay propaganda legislation set a precedent for homophobic laws worldwide. Over the past decade, Russia implemented numerous anti-LGBTQ+ laws, including the recent ban on gender reassignment. Through state-controlled media, Russian leadership portrayed both of these efforts as defending the nation against Western influence. So, I ask: Did it work? In this visualization, we see that before the introduction of anti-gay propaganda legislation and the Ukrainian conflict, public opinion data showed no statistically significant correlation between attitudes on the two issues. However, following these events, a positive association emerged between support for anti-gay views and anti-Ukrainian sentiment. These findings shed light on the growth of political homophobia within the Russian context, where gender and sexuality issues have become intertwined with a national security narrative.

LSTM and Word Embedding: Classification and Prediction of Puskesmas Reviews Via Twitter

Puskesmas is a location for top-notch volunteer work that benefits the village and city governments alike. Therefore, patient feedback regarding the kinds of services offered by the community health center is required in an attempt to improve the quality service performance. Patient opinions can be expressed through reviews or opinions about the quality of patient care on social media sites like Facebook, Instagram, Twitter, WhatsApp, and Instagram. On the other hand, thoughts shared on social media are lengthy, unstructured texts. This complicates text analysis and makes it impossible to compare the caliber of services offered by Puskesmas managers. Furthermore, a number of Community Health Centers lack websites that allow users to rank Community Health Centers according to user interest and visual appeal and efficiency in operations. Thus, the purpose of this study is to classify and present sentiment analysis from Twitter about community health centers' health services. The scope focuses on five factors: administrative services, finances, mechanisms, health worker friendliness and skills, and administrative services. The LSTM word embedding model and the adadelta and adamax optimizers are used in word embedding for text mining. A confusion matrix was used to evaluate the developed model's degree of accuracy in categorizing and forecasting patient reviews. Results from the LSTM and Adamax models with a precision level of 76%, Recall 69% and Accuracy 71%. The results of this research show that the LSTM method and Adamax optimizer can classify and predict public opinion data about Puskesmas services via Twitter quite well. A high level of accuracy is very important to ensure that community opinions can be properly identified by the model, so that it can support the decision-making process in improving the type of Puskesmas services. To improve the model, further studies can be conducted on how to select parameters, select features, and create a quality dataset.

The Resilience of Democratic Values Under Difficult Conditions. The Case of Guatemala 2012-2021

This article describes the evolution of democratic attitudes in Guatemala between 2012 and 2021 and identifies, at the political system level, the contextual factors that have contributed to changes in attitudes over time. For this purpose, we trace the linkages between recent political, economic, and social development indicators/trends and public opinion data. Based on cluster analysis of nationally representative survey data, we group of citizens with distinct patterns of democratic attitudes. Those “institutionalists”, who express consistent support for democratic institutions, comprise the largest share of respondents in all the examined years, although that share decreases in the more recent surveys. The research sustains that democratic values among Guatemalans are resistant despite the establishment’s attempts to hinder anti-corruption processes. Civic mobilization demanding accountability, justice, and respect of electoral results confirm this democratic resilience, despite the institutional setbacks.

On improbable carrots

Membership in the EU is sometimes presented as a reward for Serbia’s eventual acknowledgement of Kosovo’s independence. Although membership in the EU has been a strategic goal of Serbian foreign policy, the EU is often perceived as working against Serbian national interests regarding Kosovo’s status. Relying on the cognitive dissonance theory, we hypothesize that in addition to a direct negative association between the support for Serbia’s territorial integrity and the EU membership, there is also an interactive effect: the association should be moderated by one’s opinion on when and whether Serbia will become a member of the EU. These hypotheses are examined using public opinion data based on a large national sample of adult Serbian citizens. The results support the interaction hypothesis: the attitude that Kosovo should remain part of Serbia is a stronger predictor of the (negative) evaluation of the EU among those respondents who do not believe that Serbia will become an EU member in the future.

The Reverse Backlash: How the Success of Populist Radical Right Parties Relates to More Positive Immigration Attitudes.

What is the relationship between the electoral success of populist radical right parties (PRRPs) and public attitudes toward immigration? Previous research suggests that PRRP success can lead to more negative attitudes due to the breaking down of antiprejudice norms and more prominent anti-immigration party cues. However, we argue that greater PRRP success could have a positive relationship with immigration attitudes, reflecting negative partisanship, polarization, and a desire to reemphasize antiprejudice norms, which we call a "reverse backlash effect." Using the best available electoral and public opinion data across the last thirty years in twenty-four European countries, our TSCS analyses show the predominance of such "reverse backlash effects" across several operationalizations of PRRP success. Our argument has important consequences for the understanding of possible PRRP effects on public opinion, as well as attitudinal formation via party cueing and social norms more generally.

A fog‐computing architecture for network public opinion monitoring based on intelligent semantic recognition

AbstractCurrently, the online data contains rich user emotional information and public opinion information. These data can provide massive support for network public opinion monitoring and analysis. However, there are two problems in the network public opinion analysis of the online data. On the one hand, a vast amount of online data with discursiveness and concealment are processed in the cloud platforms, which consumes a long time. On the other hand, the massive online public opinion data is disperse and hidden, resulting in the dependence on manual screening for the analysis of public opinion. Therefore, it is still an important challenge to study the efficient and low‐latency extraction of valuable information from network public opinion. In this paper, we proposed a fog computing based framework using the technologies of intelligent semantic recognition and data mining for the analysis of network public opinion. Firstly, we build a fog computing architecture to collect the text data of network public opinion. Then, an efficient network public opinion model is constructed by intelligence semantic recognition. Finally, we achieve the function of public opinion analysis and early warning. The experimental results show that the method proposed in this paper achieves better performance against some existing methods.

Pedagogical concept and social environment matters: example from parents’ attitudes towards student-learning burden reduction policy and its influencing factors in China

In many societies, parents’ perceptions play a key role in assessing the quality of education policy implementation, and the student learning burden reduction policy is the most discussed and influential education policy among K-12 education policies in China. However, there are few studies examining parents’ attitudes toward the student learning burden reduction policy and its influencing factors. In this study, we used nationally representative data and public opinion data to identify the factors influencing parents’ attitudes towards the student learning burden reduction policy. The. Hovland’s model of attitude change have been adopted in this study. Using factor analysis and structural equation modeling, we found that, for survey analysis results, a total of 83.2% of parents supported the implementation of student learning reduction policies. Parents’ educational/pedagogical and academic concepts, students’ academic burdens, and parents’ after-school service concepts had significant influences on parents’ attitudes toward the implementation of the student burden reduction policy. Our findings show how the social implicit environment in which parents live can shape their perceptions of the implementation of the student burden reduction policy.

Development of a framework for action to advocate for a coherent prevention policy for tobacco, alcohol, and foods high in fats, salt, or sugar: a mixed-methods approach

BackgroundTobacco, alcohol, and foods high in fats, salt, or sugar (HFSS) are health harming products. Limited progress in prevention is partly due to health-harming industry lobbying. Action on Smoking and Health (ASH), Alcohol Health Alliance, and Obesity Health Alliance collaborated with the aim of developing a framework for action to address the saturation of these products in our environment. MethodsWe used a mixed-methods approach. Focus groups with academic experts, local government, and national government, recruited through snowball sampling were held in Nov 3, 2022 (14 participants); April 25, 2023 (20 participants); and June 15, 2023 (20 participants). Iteratively, data analysis was presented, and key themes tested. Commissioned economic analysis of national survey datasets quantified consumer spend on tobacco, alcohol, and food products above government recommendations (all tobacco use, >14 units of alcohol, and national dietary guidelines) and industry percentage of revenues (net of tax). Public opinion data from the ASH YouGov Smokefree Survey 2022 on a nationally representative sample of 13 088 adults were descriptively analysed for specific policy options. FindingsThe framework for action to achieve a coherent prevention approach across products included three key enablers (secure funding for prevention, a comprehensive strategy, and protecting health policy from industry interference). Five key actions were: regulate advertising to limit harm, regulate product use and environments they can be used in, promote healthy messaging, raise the price of health-harming products, and fund treatment services. Economic analysis identified 100% of tobacco usage, 43·4% of alcohol purchased, and 28·8% of food purchased by households was above government recommendations. Post-tax industry revenue was £7·3 billion for tobacco, £11·2 billion for alcohol, and £34·2 billion for HFSS foods. Strong public support for levies (5%, n=8495) and protecting health policy from industry influence (69%, n=9006) was apparent. InterpretationA coherent approach to prevention across health-harming products is effective and has public support. Strengths include the iterative process to develop the framework for action among focus groups and use of nationally representative datasets. Limitations include the snowball sampling. The findings were built into a strategy intended to inform future collaborative work in the area. FundingCancer Research UK (grant PICADV-Feb22\\100004).

Changes of people’s fertility attitudes based on analysis of online public opinion on three-child policy in China

ABSTRACT In response to the population aging, on May 31, 2021, the Political Bureau of the Central Committee of the Communist Party of China (CPC) proposed the policy that a couple can have three children and rolled out more supportive measures to further optimize fertility policies, which is another major initiative following the universal two-child policy introduced in November 2015. Currently, a series of population policy innovations have aroused great attractions among the public and triggered a hot debating on the Internet. People’s fertility attitude tendency under different related policies can reflect their current fertility intentions. Based on the fact, this paper firstly classifies the sentiment of online comment data on the three-child policy and analyzes people’s sentiment tendency toward the three-child policy from the spatio-temporal perspectives. Secondly, people’s points of view on the three-child policy are summarized by using Latent Dirichlet Allocation (LDA) thematic clustering. The reasons for the change in people’s fertility attitude tendency under different fertility policies are analyzed by comparing the change in people’s fertility attitude tendency with the change in people’s attentions. Finally, a multiple regression equation is constructed to analyze the key factors influencing people’s intention to have three children by using public opinion data and its extension data. The findings demonstrate: (1) people’s fertility attitudes toward the three-child policy are negative and similar among different regions; (2) compared to the two-child policy, the percentage of negative and neutral attitudes toward the three-child policy increases, while the percentage of positive attitudes decreases; (3) the increase in fertility costs, the deterioration of women’s employment environment, and the change in the concept of marriage and childbirth become important reasons for the negative change in people’s fertility attitudes toward different policies. Therefore, the government should take measures to reduce the burden of childbirth and guide the correct concept of marriage and childbirth to improve people’s fertility intentions.

Firearm Safety & Mental Health: A Call to ARMS (Assess and Reduce Means to Suicide)

Firearm violence is a significant public health problem. Public opinion data suggests that most Americans support evidence-based policies that can help reduce firearm violence. Even without national policy changes, nurses can refocus the conversation and make a lasting impact by acting now. Suicide is the leading cause of firearm-related deaths. Unsafe firearm storage is associated with a higher risk for self-directed violence. Nurses are uniquely positioned to promote a safety culture around firearms and safe storage practices. Nurses can assess, screen, and provide education to patients that can help decrease deaths related to firearms. This article explores how the attributes of cultural humility provide an outline and framework that can enable nurses to respond to the risk of firearm violence and take up a call to ARMS – Assess & Reduce Means to Suicide.

Why do democratic societies tolerate undemocratic laws? Sorting public support for the National Security Act in South Korea

ABSTRACT This study investigates why anti-democratic laws persist in democratic societies, focusing on South Korea's National Security Act (NSA). Enacted in 1948, the NSA restricts freedom of expression and movement to deter pro-North Korea behaviour. Despite being at odds with the tenets of liberal democracy, the act remains in place. Existing public opinion data indicates modest to strong support for the law, but measurement concerns leave much to be desired. Using a choice-based conjoint, we test the impact of democratic norms and national security rationales on various policy propositions related to the NSA, including its abolition. Results show widespread support for the Act, driven by both democratic norms and security concerns. Progressives are more likely to support revisions limiting the scope of the Act on the basis that it safeguards democracy, but they agree with conservatives that it should not be abolished. This research contributes to understanding South Korea's post-democratic transition and the balance between national security and democracy more broadly.

The relationship between vaccine acceptance and COVID-19 mortality in Europe: A Cross-Country analysis of public opinion and Epidemiological data

IntroductionMore than two years after SARS-CoV-2 emerged in Wuhan, China, COVID-19 has become one of the most severe pandemics in modern human history. Prior to the widespread availability of specific vaccines in 2021, public opinion surveys indicated significant vaccine hesitancy in 26 European countries. Materials and methodsEpidemiological data on COVID-19 morbidity and mortality for 26 European countries were collected in November 2021 and compared with the proportions of people willing to receive the SARS-CoV-2 vaccine according to public opinion surveys analysed in 2020. Correlations between various variables were calculated using the Pearson correlation test and visualized with the R programming language. ResultsPaired Student's t-test revealed no significant differences between survey results and actual vaccination rates (p = 0.9546), suggesting that the polls were good predictors. The percentage of people willing to receive the SARS-CoV-2 vaccine was significantly positively correlated with actual vaccination rates (R = 0.72, p = 0.00003) and significantly negatively correlated with the number of cumulative deaths per million people (R = -0.78, p = 0.0000024). However, there was no significant correlation with the number of cumulative cases per million people (R = -0.27, p = 0.18). These findings indicate that vaccines used in Europe were particularly effective at preventing severe COVID-19 and disease-related deaths, but did not necessarily provide strong protection against SARS-CoV-2 infection itself. ConclusionEncouraging widespread immunization through vaccination was crucial for rapidly improving the epidemiological situation in Europe.