Sentiment Toward Research Articles

Content Analysis of Judges’ Sentiments Toward Artificial Intelligence Risk Assessment Tools

Objective: to analyze the positions of judges on risk assessment tools using artificial intelligence.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data analysis assessed 381 judges’ responses to the question, “Do you feel that artificial intelligence (using computer programs and algorithms) holds promise to remove bias from bail and sentencing decisions?”Scientific novelty: The authors created apriori themes based on the literature, which included judges’ algorithm aversion and appreciation, locus of control, procedural justice, and legitimacy. Results suggest that judges experience algorithm aversion, have significant concerns about bias being exacerbated by AI, and worry about being replaced by computers. Judges believe that AI has the potential to inform their decisions about bail and sentencing; however, it must be empirically tested and follow guidelines. Using the data gathered about judges’ sentiments toward AI, the authors discuss the integration of AI into the legal system and future research.Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the legal risks of using artificial intelligence.

Textual Analysis for Public Sentiment Toward National Police Using CRISP-DM Framework

Textual Analysis for Public Sentiment Toward National Police Using CRISP-DM Framework

Classification of Public Sentiment Toward 2024 Presidential Candidates on Social Media Platform X Using Naïve Bayes Algorithm

This research examines the use of Naïve Bayes algorithm to classify public sentiment on social media X towards Indonesia's 2024 presidential candidates. Against the backdrop of the importance of presidential elections in a democracy, this research focuses on analyzing public sentiment from June to August 2023. The Naïve Bayes method was chosen to process review data about the three main candidates. The classification results provide insight into the positive and negative sentiments of the public, providing benefits for political parties and researchers in understanding public opinion. This research also enhances the understanding of sentiment classification in a political context and provides readers with a useful reference on the Naïve Bayes approach to sentiment classification. In terms of accuracy, the developed naïve bayes model shows a success rate with an accuracy of 74% for Anies Baswedan, 74% for Ganjar, and 88% for Prabowo.

Analysis of Sentiment Toward Computer Science in Pre-University Education

Analysis of Sentiment Toward Computer Science in Pre-University Education

Import Competition and U.S. Sentiment Toward China

Import Competition and U.S. Sentiment Toward China

Supplemental Material for Public Sentiments Toward the COVID-19 Pandemic: Insights From the Academic Literature Review and Twitter Analytics

Supplemental Material for Public Sentiments Toward the COVID-19 Pandemic: Insights From the Academic Literature Review and Twitter Analytics

Content Analysis of Judges' Sentiments Toward Artificial Intelligence Risk Assessment Tools

Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data analysis assessed 381 judges’ responses to the question, “Do you feel that artificial intelligence (using computer programs and algorithms) holds promise to remove bias from bail and sentencing decisions?” The authors created apriori themes based on the literature, which included judges’ algorithm aversion and appreciation, locus of control, procedural justice, and legitimacy. Results suggest that judges experience algorithm aversion, have significant concerns about bias being exacerbated by AI, and worry about being replaced by computers. Judges believe that AI has the potential to inform their decisions about bail and sentencing; however, it must be empirically tested and follow guidelines. Using the data gathered about judges’ sentiments toward AI, we discuss the integration of AI into the legal system and future research.

Populism as Dog-Whistle Politics: Anti-Elite Discourse and Sentiments Toward Minority Groups

AbstractPast research has devoted more attention to the consequences of populism for party politics and democratic governance than to its effects on public attitudes—and particularly, how populist claims interact with nationalism to exacerbate exclusionary beliefs among the public. Using online survey experiments, we examine whether exposure to populism increases out-group antipathy among Democrats and Republicans. In Study 1, we randomly assign respondents to three conditions featuring vignettes based on political speeches: a morally neutral argument, a populist critique of political elites and a morally framed anti-immigration appeal. The results demonstrate that the populist treatment generates lower feeling-thermometer ratings of minority groups than the control condition, but only among Republicans and Trump supporters. Study 2 uses a similar design to evaluate the link between left-wing economic populism and economic nationalism: the populist condition features a critique of economic elites, the nationalist condition blames China for the offshoring of US jobs and the outcome variable measures respondents’ sentiments toward China. Economic nationalism generates increased anti-China sentiment among moderate and conservative Democrats but economic populism does not. Together, these findings suggest that the effects of populism on nationalist antipathies observed in Study 1 are driven by the discursive bundling of anti-elite talk with ethno-nationalism on the political right, which stands in contrast to the decoupling of economic populism from economic nationalism on the left. The former has effectively turned populism into a form of dog-whistle politics among Republicans, and Trump supporters in particular.

Negative Sentiment Toward Recent Migrants in a Post-Colonial City: A Case Study in Hong Kong

By examining negative sentiment toward recent migrants among local residents in Hong Kong, this study fills a research gap in understanding group relations between migrants and local residents in post-colonial societies. We suggest that negative sentiments toward recent migrants among local residents in Hong Kong are the result of the society's post-colonial development, which has fostered a local identity and defined a group boundary between residents born in Hong Kong and migrants from the mainland. Linking post-colonial literature with literature on group boundaries, group threats, and scapegoating, we developed four hypotheses to explain the negative sentiments of local residents toward Chinese migrants. Using findings from data collected in 2014, we show that having close friends from mainland China, having higher income, and level of job satisfaction are all related to the level of negative sentiment toward mainland migrants in Hong Kong. Implications of the findings are discussed.

Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.

BackgroundDementia is a global public health priority due to rapid growth of the aging population. As China has the world’s largest population with dementia, this debilitating disease has created tremendous challenges for older adults, family caregivers, and health care systems on the mainland nationwide. However, public awareness and knowledge of the disease remain limited in Chinese society.ObjectiveThis study examines online public discourse and sentiment toward dementia among the Chinese public on a leading Chinese social media platform Weibo. Specifically, this study aims to (1) assess and examine public discourse and sentiment toward dementia among the Chinese public, (2) determine the extent to which dementia-related discourse and sentiment vary among different user groups (ie, government, journalists/news media, scientists/experts, and the general public), and (3) characterize temporal trends in public discourse and sentiment toward dementia among different user groups in China over the past decade.MethodsIn total, 983,039 original dementia-related posts published by 347,599 unique users between 2010 and 2021, together with their user information, were analyzed. Machine learning analytical techniques, including topic modeling, sentiment analysis, and semantic network analyses, were used to identify salient themes/topics and their variations across different user groups (ie, government, journalists/news media, scientists/experts, and the general public).ResultsTopic modeling results revealed that symptoms, prevention, and social support are the most prevalent dementia-related themes on Weibo. Posts about dementia policy/advocacy have been increasing in volume since 2018. Raising awareness is the least discussed topic over time. Sentiment analysis indicated that Weibo users generally attach negative attitudes/emotions to dementia, with the general public holding a more negative attitude than other user groups.ConclusionsOverall, dementia has received greater public attention on social media since 2018. In particular, discussions related to dementia advocacy and policy are gaining momentum in China. However, disparaging language is still used to describe dementia in China; therefore, a nationwide initiative is needed to alter the public discourse on dementia. The results contribute to previous research by providing a macrolevel understanding of the Chinese public’s discourse and attitudes toward dementia, which is essential for building national education and policy initiatives to create a dementia-friendly society. Our findings indicate that dementia is associated with negative sentiments, and symptoms and prevention dominate public discourse. The development of strategies to address unfavorable perceptions of dementia requires policy and public health attention. The results further reveal that an urgent need exists to increase public knowledge about dementia. Social media platforms potentially could be leveraged for future dementia education interventions to increase dementia awareness and promote positive attitudes.

Analyzing Online Public Sentiment Toward Corporate Crisis in the Age of Big Data and Automation

The emergence of social media has created a new environment for digital communities to share their opinions and thoughts. Particularly in times of crisis where online platforms such as Facebook and Twitter have become the first destination for people to express their thoughts and share crucial information. On the other hand, social media and its algorithms have created very large amounts of data to enable Public Relations practitioners to access the data through data mining techniques. As such, this study aims to analyze online public sentiment toward a corporate crisis using big data and automation. Unlike research in the fields of information systems and computer science, which focuses on building methodology and tools, this study demonstrates the possibilities of basic big data utilization as well as linking the findings to the foundations of social theory and public relations crisis. To reach its aims, Twitter data has been collected, analyzed and visualized to perform a text mining and sentiment analysis for analyzing public sentiment about Garuda Indonesia crisis in 2019. Drawing on current literature on public relations and crisis management in digital world, the finding suggests the need of having big data skills for public relations practitioners if they wish to survive in a fully digitized society. Finally, I expect that this project will be important to draw in new discussion for scholars in a variety of area studies, such as strategic communication, corporate communication, digital public relations and even data science.

Mining Consumers’ Sentiments Toward Halal Cosmetics: A Text Analytics Approach

Mining Consumers’ Sentiments Toward Halal Cosmetics: A Text Analytics Approach

Timing the Market When Avoiding Taxes: Evidence of Investor Sentiment Toward Corporate Tax Avoidance

Timing the Market When Avoiding Taxes: Evidence of Investor Sentiment Toward Corporate Tax Avoidance

Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets.

BackgroundDuring the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices.ObjectiveThe purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors.MethodsThe authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19–related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States.ResultsIn the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues.ConclusionsThe differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.

Comparing Public Sentiment Toward COVID-19 Vaccines Across Canadian Cities: Analysis of Comments on Reddit

BackgroundSocial media enables the rapid consumption of news related to COVID-19 and serves as a platform for discussions. Its richness in text-based data in the form of posts and comments allows researchers to identify popular topics and assess public sentiment. Nonetheless, the vast majority of topic extraction and sentiment analysis based on social media is performed on the platform or country level and does not account for local culture and policies.ObjectiveThe aim of this study is to use location-based subreddits on Reddit to study city-level variations in sentiments toward vaccine-related topics.MethodsComments on posts providing regular updates on COVID-19 statistics in the Vancouver (r/vancouver, n=49,291), Toronto (r/toronto, n=20,764), and Calgary (r/calgary, n=21,277) subreddits between July 13, 2020, and June 14, 2021, were extracted. Latent Dirichlet allocation was used to identify frequently discussed topics. Sentiment (joy, sadness, fear, and anger) scores were assigned to comments through random forest regression.ResultsThe number of comments on the 250 posts from the Vancouver subreddit positively correlated with the number of new daily COVID-19 cases in British Columbia (R=0.51, 95% CI for slope 0.18-0.29; P<.001). From the comments, 13 topics were identified. Two were related to vaccines, 1 regarding vaccine uptake and the other about vaccine supply. The levels of discussion for both topics were linked to the total number of vaccines administered (Granger test for causality, P<.001). Comments pertaining to either topic displayed higher scores for joy than for other topics (P<.001). Calgary and Toronto also discussed vaccine uptake. Sentiment scores for this topic differed across the 3 cities (P<.001).ConclusionsOur work demonstrates that data from city-specific subreddits can be used to better understand concerns and sentiments around COVID-19 vaccines at the local level. This can potentially lead to more targeted and publicly acceptable policies based on content on social media.

Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment

Accurately grading open-ended assignmentsin large or massive open online courses is nontrivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has leveraged sentiment analysis in the peer-review process to inform or validate grades or utilized aspect extraction to craft a review form from what students actually communicated. This article utilizes, rather than discards, student data from review form comments to deliver better <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information</i> to the instructor. In this article, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm, which provides a fine-grained sentiment score from text alone. We end by analyzing validity and discussing conclusions from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses.

How Cultural Environments Shape Online Sentiment Toward Social Movements: Place Character and Support for Feminism*

This study explores the relationship between offline and online contexts in social movement discourse. We test whether local place character—the cultural reputations, values, and ideals associated with a particular location—predict support for feminism expressed on Twitter. Applying topic modeling to a unique corpus describing cities’ cultural reputations, we identify five cultural attributes of U.S. cities that constitute their place character. Then, we use these measures to predict the percentage of tweets that are positive toward feminism in each location. The results indicate that while social media may connect people from geographically distant areas, online discourse is still heavily influenced by the local spaces where users reside. Cities with a place character of Revitalization have more positive Twitter sentiment toward feminism, while areas known as Local Hubs for shopping and leisure are more negative. These findings contribute to social movement scholarship by highlighting one way that offline and online contexts are connected. Far from transcending local geographies, social movement discourse taking place online remains shaped by the cultural environment where it originates.

Do Views on Sex Offending Vary by Nationality? A Comparative Analysis of Community Sentiment Toward Sex Offense Legislation in the United States and United Kingdom.

Much attention has been paid to the examination of community sentiment regarding convicted sex offenders and the policy that governs these offenders' behavior. This literature, however, has largely been absent of international comparisons of sex offender community sentiment. The current study seeks to fill this gap by drawing from the results of surveys (n = 333) conducted in both the United States (US) and the United Kingdom (UK). Results indicate that sex offender policy is generally supported in both the US and the UK. Contrary to our expectations, we found that participants from the UK were less tolerant of sex offenders residing in their neighborhoods than participants from the US. Additionally, there is support for the notion that sex offender policy holds a symbolic value for both study locations. Theoretical and practical implications of these findings are discussed.

Concerns About Automation and Negative Sentiment Toward Immigration

Across 12 studies (N = 31,581), we examined how concerns about the rise of automation may be associated with attitudes toward immigrants. Studies 1a to 1g used archival data ranging from 1986 to 2017 across both the United States and Europe to demonstrate a robust association between concerns about automation and more negative attitudes toward immigrants. Studies 2a, 2b, 2c, and 3 employed both correlational and experimental methods to demonstrate that people’s concerns about automation are linked to increased support for restrictive immigration policies. These studies show this association to be mediated by perceptions of both realistic and symbolic intergroup threat. Finally, Study 4 experimentally demonstrated that automation may lead to more discriminatory behavior toward immigrants in the context of layoffs. Together, these results suggest that concerns about automation correspond to perceptions of threat and competition with immigrants as well as consequent anti-immigration sentiment.

Abstract P551: Public Sentiment Toward Immigrants and Cardiovascular Disease Risk Among Hispanics/Latinos in the Hispanic Community Health Study/Study of Latinos

Background: News reports and hate crime data suggest that anti-Hispanic/Latino immigrant sentiment was expressed by some sectors of the U.S. public during the 2016 Presidential campaign and election. The purpose of this study was to examine the association between this period and cardiovascular disease (CVD) risk factors thought to be responsive to acute stress exposure among Hispanic/Latino adults in the US. Methods: Data were from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a prospective cohort study of Hispanic/Latino adults living in the US. The analytic sample was limited to non-pregnant adults, 35 to 74 years old, who participated in Visit 1 (2008-2011) and Visit 2 (2014-2017). The exposed were defined as participants who completed Visit 2 in 2016 and the unexposed were defined as participants who completed Visit 2 in 2014 or 2015. Cardiovascular disease risk factors included elevated depressive symptoms (CESD-10 score: 10+), current smoking, and at-risk levels of alcohol consumption (women: 7+ drinks/week; men: 14+ drinks/week). Current alcohol use (i.e., any current alcohol use) was also examined as a potential stress-related outcome. Predicted marginal risk ratio models were used to estimate incident Visit 2 outcomes as a function of exposure to the year 2016 among individuals who did not have those outcomes at Visit 1. Models were adjusted for age and insurance status at Visit 1. A priori interactions with nativity status, duration of residence in the US, and Hispanic/Latino background group were examined. Results: No statistically significant association between 2016 exposure and elevated depressive symptoms was observed in the main model, but additive interaction by Hispanic/Latino background group was observed ( p = 0.03) (Mexican and Central American background: RR adjusted : 1.28 (0.94, 1.76); Hispanics/Latinos of other background groups: RR adjusted : 0.85 (0.66, 1.09)). No association between 2016 exposure and incident smoking or incident at-risk alcohol consumption was observed and tests for interaction were not statistically significant. For incident current alcohol use, borderline trends in the main model suggested an association with the exposure (RR adjusted : 1.11 (0.99, 1.26)). Further, statistically significant additive interaction by nativity status was observed (Foreign-born: RR adjusted : 1.20 (1.06, 1.37); US/Puerto Rico-born: RR adjusted : 0.78 (0.55, 1.09)). Conclusions: Trends from study findings suggest an association between exposure to anti-Hispanic/Latino immigrant sentiment in 2016 and current alcohol use among foreign-born Hispanic/Latino adults. Given limitations of using time as a proxy for exposure to anti-Hispanic/Latino immigrant sentiment, future studies should explore more specific measurements of sentiment during this time and explore short and long-term effects of this sentiment.