Public Opinion Research Articles

How partisan news outlets frame vested interests in climate change.

This paper reports a theoretically-driven quantitative content analysis of news media discourse on climate change, its effects, and solutions to understand how US news discourse differs from widely supported scientific conclusions on global climate. Despite the dire warnings and calls to action, US public opinion on the causes and solutions to climate change remain divided. In the global context, the US's split views are anomalous and may be an artifact of the US media's coverage of the climate crisis. Anthropogenic climate change represents one of the most significant threats to our planet. Framed by Vested Interest Theory (VIT), we coded a representative sample of news discourse according to VIT's constituent variables. News sources were selected according to partisan orientation and balanced across the political divide. News articles were parsed into single sentences with source and ordering was randomized for presentation auto human coders. This allowed us to code at a granular level. Results show that not all five variables are equally present, with salience, response-efficacy, and certainty being most frequently referenced. While patterns also reflect a significant partisan divide, we also found unexpected non-linear patterns in the discourse, likely due to the rhetorical style of the sources' reporting. Overall, we conclude that climate change reporting does not reflect the scientific discourse, and that this likely fuels the idiosyncratic American debate on climate change, and its effects and solutions.

Semantic-based topic model for public opinion analysis in sudden-onset disasters

Semantic-based topic model for public opinion analysis in sudden-onset disasters

Mining and Analyzing the Evolution of Public Opinion in Extreme Disaster Events from Social Media: A Case Study of the 2022 Yingde Flood in China

Mining and Analyzing the Evolution of Public Opinion in Extreme Disaster Events from Social Media: A Case Study of the 2022 Yingde Flood in China

An improved reptile search algorithm-based machine learning for sentiment analysis

The rapid growth of mobile technologies has transformed social media, making it crucial for expressing emotions and thoughts. When making significant decisions, businesses and governments can benefit from understanding public opinion. This information makes sentiment analysis vital for understanding public sentiment polarity. This study develops a hyper tuned deep learning model with swarm intelligence and many approaches for sentiment analysis. convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), CNN-LSTM, BERT-LSTM, and BERT-CNN are the six deep learning models of the sentiment analysis using deep learning with reinforced learning based on reptile search algorithm (SA-DLRLRSA) model. The reptile search algorithm, an enhanced swarm intelligence algorithm (SIA), optimizes deep learning model hyper parameters. Word2Vec word embedding is used to convert textual input sequences to representative embedding spaces. Pre-trained Word2Vec embedding is also used to address issue of unbalanced datasets. Experimental results demonstrate that the SA-DLRLRSA model works best with accuracies of 93.1%, 94.7%, 96.8%, 96.3%, 97.2%, and 98.3% utilizing CNN, LSTM, BERT, CNN-LSTM, BERT-CNN, and BERT-LSTM.

POMM: A public opinion management model integrating network game and opinion dynamics for social networks

POMM: A public opinion management model integrating network game and opinion dynamics for social networks

An investigation of the factors associated with public trust in the Sierra Leone Police

The stability and legitimacy of police institutions rely heavily on public confidence in law enforcement. In Sierra Leone, the public's trust in the Sierra Leone Police (SLP) has been influenced by a complex interplay of factors, including colonial history, post-conflict issues, economic disparities, and ongoing accountability concerns. Despite efforts to implement reforms and initiatives like community policing, public opinion of the SLP remains divided, highlighting the need for a more comprehensive examination of trust-influencing factors. While existing studies emphasize the importance of police effectiveness, procedural fairness, openness, and responsibility, there are still gaps in understanding how these elements interact in post-conflict environments. This research utilizes a quantitative methodology, employing a cross-sectional survey to gather data from 169 SLP personnel across various ranks and departments. Using structured questionnaires, the study assessed perceptions of police performance, community policing efforts, and media impact. The data was then analyzed using Structural Equation Modeling (SEM) through SmartPLS. Findings reveal that police performance is a crucial factor in fostering public trust, with transparency and accountability also playing significant roles. Community policing initiatives showed limited success due to implementation difficulties, while media portrayals considerably shaped public perception. Ongoing issues such as corruption and historical grievances continued to undermine trust. These results emphasize the necessity for targeted reforms addressing corruption, enhancing transparency, and improving community engagement. To rebuild trust and promote collaboration between law enforcement and the public in Sierra Leone, it is essential to strengthen media strategies and effectively implement community policing initiatives.

Public opinions on social media: how to become a trustworthy leader in times of crisis

PurposeThe study compares public opinions toward several heads of state in times of crisis across countries, especially during the conflict between Russia and Ukraine. Although recent studies have primarily addressed public sentiment during the COVID-19 pandemic, critical international conflicts continue to impact economic and political dynamics that need warrant examination.Design/methodology/approachUsing social media data, this study utilizes content and sentiment analysis to assess public perceptions of leaders’ actions and communication strategies.FindingsFindings indicate that most leaders elicited predominantly negative sentiment, with only two leaders viewed favorably.Originality/valueA notable contribution of this study is the identification of communication patterns as pivotal in shaping public trust; leaders who failed to articulate a clear shared vision faced heightened negative sentiment, as ineffective communication fostered public distrust and anxiety. Although principles of ethical political leadership were observed in some responses, this study underscores that ethical leadership alone is insufficient. Transparent and strategic communication, particularly during crises, emerges as essential to aligning public perception and fostering trust, highlighting its importance as a core component of effective leadership in international conflicts.

Addressing public opinion misperceptions of racial disparities in the criminal justice system

ABSTRACT People often misperceive public opinion, challenging public engagement. Addressing mixed findings on corrective interventions, we conducted two studies on White Americans’ perception of public opinion regarding racial biases in policing practices. Majority view holders (i.e. those recognizing racial biases in policing) generally reported accurate estimates of public opinion and were unaffected by a message emphasizing public opinion statistics. However, minority view holders (i.e. those rejecting the presence of racial biases) mostly underestimated public recognition of racial biases in policing. Highlighting public opinion statistics increased this group’s awareness of racial biases, but only for those who strongly identified as Americans.

Human brain organoid research: An analysis of public attitudes and ethical concerns in Japan

Human brain organoids are three-dimensional neural structures derived from human pluripotent and tissue stem cells. They have become a focal point in scientific research due to their potential to elucidate brain functions, disease mechanisms, and therapeutic strategies. However, the ethical implications of this research have sparked significant debate, particularly concerning the potential for consciousness and effects of transplanting organoids into animals. Given the complexity and novelty of these issues, it is essential to gauge public opinion to ensure that the research aligns with societal values and ethical standards. To understand public perceptions of human brain organoid research, we conducted an online survey targeting Japanese individuals. The survey explored expectations, concerns, and agreements regarding this type of research. The results revealed high expectations for applied and clinical outcomes but significant ethical concerns, particularly regarding unanticipated risks and commercialization. Moreover, comprehension test scores were positively correlated with support for various research objectives, indicating that informed individuals may view this type of research favorably. These findings underscore the need for public engagement and informed discussions to foster responsible innovation in this emerging field.

Tocqueville on Servants, Servitude, and Impersonal Domination

Abstract Alexis de Tocqueville highlights a paradox at the heart of the democratic spirit. Egalitarians are allergic to markers of social hierarchy and are on the hunt to identify and abolish remnants of the old regime’s entrenchment of aristocratic rank. The resulting abolition of personal hierarchy, however, does not deliver equality and freedom. Rather it inclines democrats to accept depersonalized forms of discipline at the hands of public opinion, the tutelary state, and the market economy. Tocqueville argues that the commitment to an “imaginary equality” lies at the root of the soft despotism he finds across democratized institutions, mores, and economic relationships. He develops a distinct account of the emergence of domination under conditions of formal equality. Social dependence in liberal societies does not derive from a new class of elites or the rise of capitalist economic formations. Equality is itself connected to new, impersonal forms of servitude.

Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies.

The COVID-19 pandemic reshaped social dynamics, fostering reliance on social media for information, connection, and collective sense-making. Understanding how citizens navigate a global health crisis in varying cultural and economic contexts is crucial for effective crisis communication. This study examines the evolution of citizen collective sense-making during the COVID-19 pandemic by analyzing social media discourse across Italy, the United Kingdom, and Egypt, representing diverse economic and cultural contexts. A total of 755,215 social media posts from X (formerly Twitter) were collected across 3 time periods: the virus' emergence (February 15 to March 31, 2020), strict lockdown (April 1 to May 30, 2020), and the vaccine rollout (December 1, 2020 to January 15, 2021). In total, 284,512 posts from Italy, 261,978 posts from the United Kingdom, and 209,725 posts from Egypt were analyzed using the latent Dirichlet allocation algorithm to identify key thematic topics and track shifts in discourse across time and regions. The analysis revealed significant regional and temporal differences in collective sense-making during the pandemic. In Italy and the United Kingdom, public discourse prominently addressed pragmatic health care measures and government interventions, reflecting higher institutional trust. By contrast, discussions in Egypt were more focused on religious and political themes, highlighting skepticism toward governmental capacity and reliance on alternative frameworks for understanding the crisis. Over time, all 3 countries displayed a shift in discourse toward vaccine-related topics during the later phase of the pandemic, highlighting its global significance. Misinformation emerged as a recurrent theme across regions, demonstrating the need for proactive measures to ensure accurate information dissemination. These findings emphasize the role of cultural, economic, and institutional factors in shaping public responses during health crises. Crisis communication is influenced by cultural, economic, and institutional contexts, as evidenced by regional variations in citizen engagement. Transparent and culturally adaptive communication strategies are essential to combat misinformation and build public trust. This study highlights the importance of tailoring crisis responses to local contexts to improve compliance and collective resilience.

Public Opinion Evolution Based on the Two-Dimensional Theory of Emotion and Top2Vec-RoBERTa

This paper applies the concept of symmetry to the design of a research methodology for public opinion evolution, emphasizing that both the construction and analysis processes of the method embody symmetrical principles. In today’s information age, dominated by social media, online platforms have become crucial venues for information dissemination. While the free flow of information promotes public participation, it also introduces certain challenges. Therefore, analyzing the evolution of public opinion and extracting public sentiment holds significant practical value for managing online public sentiment. This study takes the Zibo barbecue incident as a case study, utilizing the two-dimensional theory of emotion and Top2Vec for thematic analysis of public opinion comments. By combining sentiment dictionary methods with the RoBERTa model, we conduct a sentiment polarity analysis of public opinion comments. The results show that the RoBERTa model achieved an accuracy of 98.46% on the test set. The proposed method effectively uncovers public sentiment biases and the influencing factors on public emotions during the evolution of public opinion events, providing a more comprehensive understanding of the emotional dynamics throughout the development of public sentiment. This deeper insight aids in addressing issues related to public opinion more effectively.

News Media Effects on Policy Priorities: A Second-Level Agenda-Setting Analysis of Belt and Road Initiative (BRI) Projects in Myanmar

This study investigates the impact of the media’s secondary agenda-setting on public perception and policy priorities regarding the Belt and Road Initiative (BRI) projects in Myanmar from to 2011–2024. Three hypotheses (Hs) were examined: H1 posits that media coverage aligns with public and policy perspectives; H2 proposes that media emphasis on specific attributes influences their prominence in public opinion and policymaking; and H3 suggests that public opinion mediates the relationship between media coverage and policy priorities, indicating an indirect media influence on policymaking. This study employed a cross-sectional research design, utilizing both quantitative content analysis and survey methodologies. The content comprises the analysis of two news outlets’ coverage (n = 144), surveys with 385 respondents (n = 385), and in-depth interviews with 30 participants (n = 30), and the findings revealed a significant positive correlation between media coverage and public opinion and policy priorities (r = 0.86, r = 0.81, respectively). SEM path analysis reveals that the media agenda indirectly shapes the policy agenda through the public agenda, with an indirect effect path coefficient of 0.718, highlighting the media’s role in policymaking processes. This is evidenced by a significant correlation between media coverage and public opinion (p < 0.05), and a strong direct association between public opinion and policy priorities (p < 0.05). This study extends the agenda-setting theory by empirically demonstrating the media’s indirect effect on policy formation in global infrastructure projects within a developing Southeast Asian country. Future studies should examine the evolution of media patterns across different administrations, including the current one. The results also suggest examining the broader media portrayal of governance issues and conducting extensive research on the media’s impact on public opinion.

User Profile Construction Based on High-Dimensional Features Extracted by Stacking Ensemble Learning

Online social networks, as platforms for personal expression, have evolved into complex networks integrating political and social dimensions. This evolution has shifted the focus of network governance from addressing hacking activities to mitigating unpredictable social behaviors, such as the malicious manipulation of public opinion, the doxing of ordinary users, and cyberbullying. However, the sparsity of data and the concealed nature of user behavior pose significant challenges to existing network reconnaissance technologies. In this study, we focus on constructing user profiles on online social network platforms by extracting features to build deep user profiles based on behavioral patterns. Drawing inspiration from the 5Cs principle of credit evaluation, we refine it into a 3Cs principle tailored for user profiling on social network platforms and associate it with user behavioral patterns. To further analyze user behavior, a high-dimensional feature extraction method is proposed using an improved stacking ensemble learning model. Based on experimental data analysis, the most suitable base algorithms for high-dimensional feature extraction are identified. Experimental results demonstrate that the integration of high-dimensional features improved the behavior prediction accuracy of the profiling model by 9.26% on balanced datasets and enhanced the AUC (area under the curve) metric by 3.69% on imbalanced datasets. The proposed method effectively increases the depth and generalization performance of user profiling.

Framing the harms of wrongful convictions: how different narratives shape public opinion

Framing the harms of wrongful convictions: how different narratives shape public opinion

Mining population opinion about local police

Abstract Sentiment analysis, or opinion mining, is an important task of natural language processing (NLP) that extracts opinions, attitudes, and emotions from text. With the growth of digital platforms like blogs and social networks, opinion mining has become a key tool for organizations to understand public sentiment. In recent research, machine learning and lexicon-based approaches have been applied to analyze sentiments. Our work specifically focuses on national security, where sentiment analysis offers crucial insights into local opinions, helping authorities gauge public mood. As part of our research, we developed the Public Sensing about Police Platform, a prototype system designed to analyze emotions from social networks. This system generates dashboards for law enforcement and security agencies, providing actionable intelligence for public safety. Our findings show that “Hate” was the most common emotion expressed in relation to police interventions, indicating widespread unpopularity of these actions and a resulting sense of insecurity among the public.

Emotionalization of the 2021–2022 Global Energy Crisis Coverage: Analyzing the Rhetorical Appeals as Manipulation Means in the Mainstream Media

As the issues of the world’s overreliance on fossil fuels still remain unresolved, mainstream media play a central role in influencing public attitudes towards energy sources. This article aimed to consider Aristotle’s rhetorical appeals as manipulation means in the news coverage of the 2021–2022 global energy crisis. Using computer-aided text analysis, media framing, discourse, and rhetorical analyses, this paper analyzes 600 news articles published on the websites of the four mainstream media sources from the key countries that were affected by the crisis. The results confirmed emotionalization of the news coverage that occurred through the use of similar rhetorical appeals and emotive language means aimed at inducing positive or negative feelings and shaping public opinion. The UK and US mainstream media appeared to rely more on the opinions of political, business, and energy authorities, highlighting a high level of politicization of their coverage. The findings also indicated polarization of the attitudes in the coverage, with mainly negative narratives about fossil fuels and more positive narratives about renewable energies, which contributed to public opinion manipulation and energy decision-making. This study opens up perspectives for future research on media emotions and rhetorical appeals as powerful manipulation means in applied linguistics, rhetoric, and journalism.

Public opinion in early modern Scotland, c. 1560–1707

Public opinion in early modern Scotland, c. 1560–1707

How Advocacy Messages Influence Support for “Ban the Box” in College Admissions

ABSTRACTObjectivesThis paper examines public attitudes toward the “Ban the Box” policy in college admissions in Georgia. It investigates how exposure to framed messages affects support for or opposition to the policy, focusing on its potential impact on campus safety and overall effects.MethodsThe data are from independent survey experiments conducted on two samples: undergraduate students at a USG institution (n = 587) and adult residents in Georgia (n = 500). Participants were randomly assigned to one of four experimental conditions: control, support banning the box message, oppose banning the box message, or mixed/competitive frame message.ResultsParticipants exposed to supportive messages showed increased support, while those exposed to opposing messages showed decreased support. Competitive framing had mixed effects, with no significant difference from the control group in the adult sample. Overall, positive advocacy frames counteracted the negative impact of oppositional messages.ConclusionsThe study highlights the importance of framing in shaping public opinion on banning the box policies in college admissions. Both supportive and oppositional frames effectively sway attitudes, indicating the complexity of public perception on this issue. Further research is needed to explore the persistence of framing effects and their implications in policy debates.

Research on characteristics mining and management strategies for key rumor spreaders

PurposeIn the era of social media, containing and managing online rumors poses a significant challenge. Therefore, a thorough investigation into the factors influencing rumor dissemination becomes increasingly crucial. Key rumor spreaders wield considerable influence over audience behavior and public opinion dissemination, making them pivotal in curbing the spread of rumors. This study aims to categorize these key spreaders and delve into the distinct characteristics of each category.Design/methodology/approachThis paper introduces a method for identifying key rumor spreaders through social network analysis. By utilizing text mining and sentiment analysis on data from typical rumor events on Weibo, we extract 14 characteristics of key spreaders. Subsequently, we develop a method for classifying the roles of these key spreaders and conduct empirical research to validate our findings.FindingsOur study reveals significant variations in the characteristics of key spreaders across different roles. These insights enable managers to implement differentiated management strategies for key spreaders based on their respective roles.Originality/valueThis research sheds light on the diverse characteristics exhibited by key rumor spreaders on social media, providing a novel reference point for enhancing the effectiveness of rumor intervention and control strategies.