Public Opinion Research Articles

Introduction: Regional inequality and political discontent in Europe

ABSTRACT What is the relationship between regional inequality and political discontent in Europe? This special issue addresses this question by studying the influence of the subnational context. Specifically, it examines the impact of unequal social conditions and economic growth on political discontent with national and international politics, as expressed in political behaviour, public opinion and policy preferences. This introduction outlines a research agenda that establishes the context of the special issue. It explains the key concepts of interest, summarises significant empirical trends, outlines explanatory models and discusses methodological challenges. In addition to establishing the research agenda that serves as the analytical framework, this introduction also presents the core findings of the special issue and discusses broader implications for future research and practice.

Polarization, the Pandemic, and Public Trust in Health System Actors.

Public opinion on the performance of health system actors is polarized today, but it remains unclear which actors enjoy the most (least) trust among Democrats and Republicans, whether the Covid-19 pandemic has influenced how people view their own physicians, and whether doctors have retained the ability to influence public beliefs about policy issues. We conducted two national surveys in 2022 and 2023 to examine these questions. Democrats rate the performance of medical research scientists and public health experts during the pandemic more highly than do Republicans and independents. About three in ten Republicans say that the pandemic decreased their trust in their personal doctors. Nonetheless, most Americans report confidence in physicians. We replicate the findings of Gerber et al. (2014) to demonstrate that respondents continue to have more positive views of doctors than other professionals, and that public opinion is responsive to cues from a doctors' group. What polarizes Democrats and Republicans today is not whether medical scientists and public health experts are competent, but whether the advice offered by these actors is in the public interest and should guide policymakers' decisions. Democrats strongly believe the answer to these questions is yes, while Republicans exhibit skepticism.

Impervious to Elite Influence: Americans' ACA Attitudes, 2009-2020.

The Affordable Care Act (ACA) was a central issue dividing Republicans and Democrats for the decade following its 2010 enactment. Given that, it offers key lessons about policymaking and public opinion during a highly polarized political period. Here, I draw out some of those lessons from my 2023 book Stable Condition: Elites' Limited Influence on Health Care Attitudes, detailing how polarization shaped both the elite- and mass-level politics of the ACA. At the elite level, polarization and nationalization within the federal and state governments laid the groundwork for a highly complex law that was a patchwork of policies experienced very differently by different Americans. At the mass level, polarization and nationalization contributed to a remarkable level of stability in public opinion, so much so that even direct beneficiaries of the law did not typically become markedly more positive toward it. Elite efforts at opinion leadership, through policymaking and messaging, were largely unsuccessful.

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.

The social media sentiment analysis framework: deep learning for sentiment analysis on social media

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Purpose This study aims to use geographical information on social media for public opinion risk identification during a crisis. Design/methodology/approach This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs. Findings In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced. Originality/value Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

Conditional Enfranchisement: How Partisanship Determines Support for Noncitizen Voting Rights

Expanding suffrage is critical for democratic inclusion. In the United States, noncitizen residents are the latest focus of such (re)enfranchising efforts. Public opinion plays a significant role in the passage of legislation expanding or restricting noncitizen access to local elections. Although elite support for noncitizen suffrage is well-documented, little is known about public opinion toward such noncitizen voter policies. What accounts for voter support for noncitizen electoral participation? We argue that the partisan alignment between noncitizens and U.S. voters shapes U.S. voters’ support for noncitizen voting rights. Evidence from two survey experiments suggests that U.S. voters are pragmatic in their enfranchising preferences: voters increase their support for co-partisan enfranchisement but oppose this same policy when considering out-partisans. These dynamics are present among both Republicans and Democrats, underscoring the societal implications of heightened partisanship on American democracy.

Automatic Content Analysis Systems: Detecting Disinformation in Social Networks

In the 21st century, the rapid rise of disinformation and propaganda has become a significant global issue, undermining democratic processes and socio-political institutions. Disinformation, defined as intentionally false or misleading information, aims to manipulate public opinion and cause economic harm. This paper explores the use of computational linguistics and machine learning methods to detect disinformation. Techniques such as text preprocessing, feature extraction, and classification algorithms (e.g., SVM, naive Bayes) are adapted for identifying fake news. Recent studies demonstrate the effectiveness of these methods in social media and news platforms, highlighting the importance of advanced models like GPT-4 in improving detection accuracy and combating the spread of disinformation.

Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis

Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment.

Attributions of Sexual Assault: Effects of Victim and Perpetrator Stereotypes, Presentation Order, and Participant Characteristics.

Research on sexual assault has shown that victim, perpetrator, and participant characteristics can influence evaluations of an assault. However, the studies have not examined a possible main effect or interactions from respectively introducing the victim or perpetrator first in an assault description, and previous studies have used participant samples with little diversity. We conducted two studies with factorial between-groups designs that varied presentation order in addition to victim and perpetrator stereotypicality and then assessed the impacts on participants' judgments of sexual assault scenarios. We used the online marketplace Prolific to collect large, diverse samples of participants, and in the second study, we collected roughly equal sample sizes of individuals who identified as Black females/males and White females/males. Our results indicate that multiple factors-including victim and perpetrator stereotypicality, presentation order, and participants' gender identities-significantly influenced judgments of the sexual assault, and there were numerous interactions. The results provide strongest support for a spreading activation model in which each factor can influence a participant's judgment of the other factors and the overall scenario. As such, the findings may bear on the legal handling of sexual assault cases as well as suggesting how different presentation formats and emphases in media coverage may sway the court of public opinion.

Tolerance for corruption and descriptive social norm: An experimental study of embezzlement.

Public tolerance for corruption within a society significantly influences the prevalence of corrupt practices, but less is known about how this tolerance evolves with social norms. This paper presents experimental evidences demonstrating that the descriptive social norm indicating widespread corruption can lead to increased tolerance for corruptive acts. We introduce an asymmetric information ultimatum game to simulate the interactions between embezzlers and citizens. Game theoretical analysis reveals that victims anticipating corruption will exhibit greater compliance with embezzlement when the offers are evaluated based on descriptive norms. To test the hypothesis, we employ a framing effect to induce variations in descriptive norms within a behavioral experiment. Although the treatment effect is significant only in the subgroup of student cadres, this subgroup demonstrated increased beliefs about embezzlement, greater tolerance for corruption, and a heightened propensity to embezzle when exposed to framings with hierarchical implications. This paper contributes to the corruption literature by examining the effects of descriptive norms on victims' responses to embezzlement. It offers a more comprehensive perspective on how social standards shape public opinions and corrupt actions, enhancing our understanding of the self-reinforcing nature of corruption.

Behavioral Patterns of Social Media Users toward Policy: A Scientometric Analysis

This research explores the behavioural patterns of social media users in response to policies, bridging a gap in the literature by providing in-depth insights into the interaction between user behaviour and public policies relating to social media. An important contribution of this research lies in its holistic approach to understanding how social media users respond to various public policies and their impact on their behaviour. This research aims to explore the influence of the interaction between social media and public policy, understanding how user behaviour on these platforms affects responses to government policies. This study uses qualitative research methods with a scientometric approach. The main results underline the crucial role of social media not only as a communication channel but also as a significant influence tool in shaping public opinion and policy. These studies show that social media has a dual potential, which can support professional and community development, as well as influence government policies and actions through social interaction and information dissemination. However, negative impacts are also evident, such as in the spread of fake news that can significantly alter political views. The practical implications of this research emphasise the need for proactive and systematic government strategies in using social media for the dissemination of accurate information and combating fake news, thereby improving the effectiveness of policy communication and responsiveness to public needs.

No Peace Without Punishment? Reintegrating Islamic State “Collaborators” in Iraq

Abstract How does variation in the severity of punishment affect public opinion toward the reintegration of former enemy “collaborators” after war? We study this question in the context of Iraq where the Islamic State, an armed rebel group, captured and governed a population of more than five million Iraqi citizens between 2014 and 2017. Building upon extensive fieldwork and interviews in Iraq, we designed a survey experiment that randomly varied the severity of sentences in hypothetical scenarios of civilians who supported the Islamic State in non-violent roles (e.g., cleaners, cooks, and wives of fighters). The experiment allows us to estimate the causal effects of punishment on attitudes toward reintegration. We find that a long prison sentence (fifteen years) does not increase the likelihood of participants’ willingness to allow the reintegration of former collaborators; instead, a noncarceral punishment (community service) has a small but statistically significant positive effect. Our most striking findings show that noncarceral and community-based justice mechanisms can significantly increase the likelihood of successful reintegration after punishment. Fifteen percent of respondents who were initially opposed to the return of former collaborators into their communities said that they would be willing to change their judgment and support reintegration if they were asked to do so by a tribal or religious leader, or if the offender completes a noncarceral rehabilitation program. These findings suggest that noncarceral, restorative, and community-based justice mechanisms may be equally or potentially more effective than long-term incarceration for achieving the objectives of rehabilitation and eventual reintegration of former nonviolent offenders. This Article advances the field of comparative empirical legal scholarship by providing an innovative experimental research design that can be replicated by scholars studying other contexts to help answer important questions about the causal effects of criminal justice policies.

Real-Time Sentiment Analysis System Using the BERT Model

Opinion mining or we can say the Sentimental analysis is a critical issue where a large amount of information related to public opinion is widely spread in the world. So, in this paper, we will try to understand the public opinion and decision-making process in real time for the given text data. This proposed system aims to develop a real-time sentiment analysis of text data from various sources, such as social media, news websites, etc. The system will involve several processes like data collection, data preprocessing, sentiment analysis using the BERT (Bidirectional Encoder Representation from Transformers) model, and real-time processing to find the sentiment trends and patterns. based on [4] we can say that the performance of the BERT model is highly accurate as compared to the other traditional models like Random Forest, KNN, and Decision Tree, etc. The result which is obtained using the BERT model shows the accuracy of the system.

Blockchain technology, social media sentiment and stock price

AbstractWe investigate the impact of the announcements by firms to adopt blockchain technology on stock prices and the different characteristics of stock prices under different types of blockchain technologies. Simultaneously, we propose two impact mechanisms: one is investor sentiment, and the other is the firm's governance of social media. We find that the firm's stock price has shown an increasing trend since the release of the announcement of the adoption of blockchain technology. At the same time, firms engaged in blockchain research, development and investment, and that simultaneously deploy other financial technologies, as well as firms that deploy blockchain technology for non‐speculative purposes, have better stock prices. In addition, the layout of a firm's blockchain may affect its stock price by influencing investor sentiment and public opinion.

Temporal Dynamics of Online Sentiments: Investigating the Impact of Real-World Events on #StopAsianHate Twitter Movement

Stop Asian hate has been a heated topic on social media platforms because of the increase in Asian hate after the breakout of COVID-19 and hate crimes caused by Asian hate. The hashtag #StopAsianHate on Twitter points out that making proper consensus decisions can be an effective way to spread the impact of hate crimes and attract more attention to Asian hate to call for actions to stop Asian hate. To gain more theoretical knowledge for consensus decisions and guidance of public opinion, this study used MDCOR and tools developed by our researchers to analyze posts under tweets (opened answers) posted under the hashtag #StopAsianHate (from March 1, 2022, to August 31, 2023). The tools being used can directly analyze the open answers on Twitter, providing the study with the identification of topics and sentiment analysis. Through the analysis of 5-month data, which is far longer than other research, the dynamic of the publics opinion and sentiment is clearly shown. By understanding the shifts in public opinion and sentiment, more valid strategies for advocacy of anti-racism are hoped to be found.

Public Preferences Over Changes to the Composition of Government Tax Revenue

Abstract How governments raise tax revenue is at the core of domestic political conflict. Public opinion towards taxation is measured generally and qualitatively by many surveys, but previous research has not closely linked public preferences to the budget problem faced by governments regarding how best to raise or cut a marginal quantity of revenue. We present results from a novel tax preference experiment in which UK respondents are given choices over different tax ‘levers’ that are expected to raise or cut equal revenue. We find that while different tax levers vary substantially in their popularity, there is a ‘hidden consensus’ regarding different tax levers across income levels and partisanship of the respondents.

Assessing The Impact of Organizational Context on Learning Barrier Formation in Lima’s Private Universities

This research focuses on exploring the relationship between organizational learning barriers and organizational context in Private Universities in Lima, with the primary aim of elucidating how these elements interact and affect the learning dynamics within these institutions. Adopting a correlational cross-sectional non-experimental approach, a specific survey was designed and validated for application in the Peruvian context, gathering responses from 384 collaborators. The findings reveal that the most frequent barriers related to thought-action coherence highlight concerns about authenticity and sincerity in discussions, as well as a significant discrepancy between public and private opinions of workers. On the other hand, in the realm of organizational context, the critical factors identified include employees' perceptions of replaceability, fear of evaluations, deficiencies in communication channels, and the predominance of opinions from those with greater power. The conclusions point to the need to address these barriers to foster a more conducive environment for organizational learning, suggesting that effective knowledge and organizational environment management are fundamental for institutional development and adaptability.

South Korean Cost Sensitivity and Support for Nuclear Weapons

How do the costs of proliferation shape the public support for building nuclear weapons? Public opinion matters, especially in a democracy, because the masses can affect the nuclear policy choices that political elites make. Yet the existing literature on nuclear proliferation has not incorporated the public’s cost sensitivity in the analysis. We therefore do not fully understand the sources of public attitudes toward nuclearization. By fielding a novel survey experiment in South Korea, I found that the economic and human costs of economic sanctions and preventive strikes dampen South Koreans’ preferences for nuclear armament. Relative to the no-cost condition, economic and human costs reduce the support levels by 25.1% points and 19.4% points, respectively and in the case of combined costs the support decreases by 29.6% points. However, South Koreans still have relatively high levels of support for a nuclear arsenal even after the exposure to the costs of nuclearization. This implies that the rally effect resulting from hostile international responses may mitigate the negative cost effects to some degree. This study can contribute to the growing literature by providing new theoretical foundations and empirical results.

The Wrong Winners: Anti-Corporate Animus and Attitudes Towards Trade

Abstract Globalization creates winners and losers, and recent research emphasizes that large corporations are among the biggest beneficiaries of trade while smaller firms may be harmed. How do these redistributive effects impact trade attitudes? Because a growing share of Americans hold highly unfavourable views of big corporations, we argue that the belief that large firms win from trade will provoke hostility towards trade and globalization. To test this theory, we show experimentally that informing people that large corporations benefit from trade makes them markedly more hostile towards trade compared to a treatment emphasizing that firms in exporting industries benefit. Using subgroup and mediation analysis, we find that anti-corporate sentiment drives this effect, particularly concern about corporations’ power in society. Our findings illustrate how distributive consequences and attitudes towards the winners and losers from policy change interact to shape public opinion on economic policy.