Politics Go "Viral": A Computational Text Analysis of the Public Attribution and Attitude Regarding the COVID-19 Crisis and Governmental Responses on Twitter

The U.S. confronts an unprecedented public health crisis, the COVID-19 pandemic, in the presidential election year in 2020. In such a compound situation, a real-time dynamic examination of how the general public ascribe the crisis responsibilities taking account to their political ideologies is helpful for developing effective strategies to manage the crisis and diminish hostility toward particular groups caused by polarization. Social media, such as Twitter, provide platforms for the public's COVID-related discourse to form, accumulate, and visibly present. Meanwhile, those features also make social media a window to monitor the public responses in real-time. This research conducted a computational text analysis of 2,918,376 tweets sent by 829,686 different U.S. users regarding COVID-19 from January 24 to May 25, 2020. Results indicate that the public's crisis attribution and attitude toward governmental crisis responses are driven by their political identities. One crisis factor identified by this study (i.e., threat level) also affects the public's attribution and attitude polarization. Additionally, we note that pandemic fatigue was identified in our findings as early as in March 2020. This study has theoretical, practical, and methodological implications informing further health communication in a heated political environment. [ FROM AUTHOR] Copyright of Social Science Computer Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public's response is through Twitter's social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation- based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately © 2023 The Authors. Published by Universitas Airlangga.

A Little More Respect

According to research from GlobeScan, stakeholders have a tendency to believe that the pharma industry's reputation is poor, with a particularly negative environment seen in Canada and the United Kingdom caused by campaigns from nongovernmental organizations or films (2). Great effort required As pharma companies continue to forge ahead, collaborating with others that may be considered as competitors in a 'normal' situation, there is a chance that the overall reputation of the industry can be further improved. PatientView, "Pharma Reputation in 2018Among UK Patient Groups-Falls Due to Brexit and Other Factors," Press Release, 16 July 2019.

A survey of automatic contact tracing approaches using bluetooth low energy

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.Copyright © 2021 ACM.

A social psychological investigation of community sentiment toward COVID-19 mitigation policies in the justice system

The COVID-19 pandemic led to many policy changes across the U.S. justice system that aimed to reduce the spread of the deadly virus. The present dissertation provides novel insights into community sentiment toward justice system COVID-19 mitigation policies such as the early release of prisoners, the pretrial release of defendants, the suspension of fines and fees, and the prioritization of prisoner vaccination. Using a student sample (study 1) and a demographically-representative U.S. community sample (study 2), this dissertation found that political conservatism was negatively associated with support for justice system COVID-19 mitigation policies across both samples. Prison reform attitudes and COVID-19 anxiety were also positively associated with support for justice system mitigation policies in the community sample. In addition to exploring direct relationships, this research examined mechanisms between political conservatism and support for justice system COVID-19 mitigation policies. The results provide evidence that people high in political conservatism show low support for justice system COVID-19 mitigation policies because of authoritarian attitudes and their moral disengagement from those in the justice system. The results of this research contribute to the growing literature on how individual differences can affect COVID-19 pandemic-related attitudes. They also provide policymakers with an idea of how to tailor a more effective public health strategy to promote the welfare of one of the most vulnerable populations to public health crises - those involved in the justice system. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Research on Key technologies of network public opinion warning based on improved Stacking algorithm

Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.

Relevance of social capital in preserving subjective well‐being in the face of the COVID‐19 pandemic

This paper analyzes the changes in subjective well‐being (SWB) in 11 Latin American cities at the end of the acceleration phase of the COVID‐19 pandemic, the variables that influenced these changes, and the role of the public support policies and the social capital on SWB recovery. This study, the second of a two‐phase research project, is a survey‐based comparative analysis. The 5604 survey responses obtained included 3279 observations from the research project's preceding phase, and 2325 observations conducted as part of the second project phase and current study. A multivariate linear regression model was used to evaluate the impact of the different variables related to COVID‐19 on people's SWB. Results show that the most significant positive impacts on SWB, are attributed to social capital, particularly family and social cohesion. The study confirms that the level of SWB is strongly associated with personality traits, health, and key variables such as age, marital status, and income. The different measures established to control the pandemic have not remedied the negative impacts of COVID‐19 on people's SWB. However, an increase in different degrees of SWB was observed in 10 out of the 11 cities between the initial survey and the follow‐up survey.Alternate :本文分析了11个拉丁美洲城市在2019冠状病毒病（COVID‐19）大流行加速阶段结束时的主观幸福感（SWB）变化、影响SWB变化的变量、以及公共支持政策和社会资本对SWB恢复发挥的作用。本研究是一项由两阶段组成的研究课题的第二阶段——一项基于调查的比较分析。获得的5,604份调查回复包括来自研究课题第一阶段的3,279次观察，以及课题第二阶段（本研究）进行的2,325次观察。使用多元线性回归模型评价与COVID‐19相关的不同变量对人民SWB产生的影响。结果表明，对SWB产生最显著积极影响的变量为社会资本，特别是家庭和社会凝聚力。本研究证实，SWB水平与人格特征、健康状况、以及年龄、婚姻状况和收入等关键变量强烈相关。为控制大流行而制定的不同措施并没有弥补COVID‐19对人民SWB产生的负面影响。不过，从初次调查到后续调查的期间，11个城市中有10个城市的SWB出现了不同程度的上升。Alternate :RESUMENEste artículo analiza los cambios en el bienestar subjetivo en 11 ciudades de América Latina al final de la fase de aceleración de la pandemia de COVID‐19, las variables que influyeron en estos cambios y el papel de las políticas públicas de apoyo y el capital social en la recuperación del bienestar subjetivo.Este estudio, el segundo de un proyecto de investigación de dos fases, es un análisis comparativo basado en encuestas. Las 5605 encuestas respondidas incluyeron 3279 observaciones de la fase anterior del proyecto de investigación y 2325 observaciones realizadas como parte de la segunda fase del proyecto y el estudio actual. Se utilizó un modelo de regresión lineal multivariado para evaluar el impacto de las diferentes variables relacionadas con COVID‐19 en el bienestar subjetivo de las personas.Los resultados muestran que los impactos positivos más significativos en el bienestar subjetivo se atribuyen al capital social, particularmente a la cohesión familiar y social. El estudio confirma que el nivel de bienestar subjetivo está fuertemente asociado con los rasgos de personalidad, la salud y variables clave como la edad, el estado civil y los ingresos. Las diferentes medidas establecidas para el control de la pandemia no han subsanado los impactos negativos del COVID‐19 en el bienestar subjetivo de las personas. Sin embargo, se observó un aumento en diferentes grados de bienestar subjetivo en 10 de las 11 ciudades entre la encuesta inicial y la encuesta de seguimiento.

Public Attitudes to Immigration in the Aftermath of COVID-19: Little Change in Policy Preferences, Big Drops in Issue Salience: IMR

How has the COVID-19 pandemic affected public attitudes toward immigration? Long-term evidence in Europe and the United States suggests attitudes to immigration are relatively stable and, in some cases, becoming more favorable with high volatility instead of the perceived importance of the issue. However, theoretically a global pandemic could exacerbate people's fears of outsiders or that migration may contribute to the disease. By contrast, attitudes could remain stable if their distal drivers prove to be robust enough to withstand the shock of COVID-19. We draw from Eurobarometer data from 2014 to 2021 across 28 European countries, weekly national survey data during the outbreak from the United States and individual panel data from the United Kingdom and Germany to find little systematic change in immigration preferences and no country-level correlation between the observed changes and the outbreak's severity. Instead, the perceived importance of immigration has consistently and significantly decreased. These findings suggest that, if COVID-19 is to have an impact on attitudes to migration, it is likely to emerge via longer-term means, such as early-life socialization and value change, rather than reactions to the immediate pandemic shock.

Expendable to essential? Changing perceptions of gig workers on Twitter in the onset of COVID-19

While ride-hailing ridership declined in 2020 due to COVID-19 induced restrictions like stay-at-home orders, food/grocery delivery services became quasi-essential. This study investigates if and how public perceptions of gig work related to platform-based ride-hailing and food/grocery delivery services changed during the early stages of the pandemic. We collected a sample of 23,845 Twitter posts ('tweets') related to these platform-based services within two-week periods before and after the US COVID-19 emergency declaration. Sentiment analysis on tweets was conducted to investigate changes in public perception of gig work. Tweet content was analyzed by descriptively coding about 10% of the sample of tweets manually along ten different dimensions (e.g., personal experience, informative, and about driver);then we used thematic analysis to gain an understanding about the public's views towards gig work/workers. We tested supervised machine learning methods to explore their potential to classify the rest of the sample along the ten descriptive dimensions. The number of tweets increased by approximately 150% after the emergency declaration and became more positive in sentiment. Qualitative results indicate that tweets about negative personal experiences with drivers/companies decreased during COVID-19, while tweets exhibiting a sense of community (e.g., sharing information) and concern towards gig workers increased. Findings can inform policy and workforce changes regarding platform-based service companies. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic. © 2021 Association for Computing Machinery.

The impact of COVID-19 on the social image of Nursing

Objective: to describe the current perception of Nursing by society, and to understand how the representation of the Nursing profession in the social consciousness has changed with the healthcare emergency scenario of COVID-19. Method: a descriptive cross-sectional study in the overall population andhealthcare professionals, recruitedthroughnonprobabilistic sampling (the sample size achieved, n= 1.109, delivered absolute accuracy of 2,94%), through a questionnaire designed ad hoc. Results: Nursing was considered a profession devoted to care andessentialwithin the healthcare system (83.7%), butwithlow socialrecognition (53.8%), underpaid (48.4%)and perceivedas a high-risk profession (93.3%). These professionals were considered to have university degree education (84.8%), and their most outstanding quality was knowledge (85.2%). The stereotype profile was a woman (92.8%), middle-aged (53.9%), wearing sanitary "pyjamas” (85.3%). 52.4% of the sample believed that the COVID-19 pandemic had changed their perception of Nursing;the importance of these professionals was acknowledged, andtheir performance was valued positively, inspiring feelings of admiration and respect towards them. Conclusions: Nursing has been considered a profession devotedto care andessentialwithin the healthcare system, but with low social recognition, underpaid, and perceived as a high-risk profession. The healthcare emergency experience has given visibility to nursing care. There has been a positive impact by the COVID-19 pandemic upon the social image of Nursing, revaluing it as a profession. © 2022 DAE Editorial, Grupo Paradigma. All rights reserved.

Unexpected, but consistent and pre-registered: Experimental evidence on interview language and Latino views of COVID-19

Much uncertainty remains about effective messaging to boost public support for COVID-19 mitigation efforts, especially among people of color. We investigate the relationship between interview language and expressed support for COVID-19 public health protocols among Latinos: America's largest ethnic group. Prior work establishes that interview language shapes opinions by cognitively structuring which considerations people use to express attitudes. Yet other work suggests interview language shapes opinions by activating specific cultural norms associated with a tongue. We predicted that interviewing in Spanish (versus English) would boost support for COVID-19 protocols by activating pro-social norms known to be strongly associated with that language. We uncover null support for this prediction in a pre-registered experiment on bilingual Latino adults (N = 1645). Instead, we find that Latinos assigned to interview in Spanish report weaker support for COVID-19 protocols, regardless of which cultural norms are primed. We discuss implications for COVID-19 attitudes in linguistically diverse polities.

Mixed frequency composite indicators for measuring public sentiment in the EU.

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

Awareness of COVID-19 at the Local Level: Perceptions and Political Consequences.

CONTEXT: While the COVID-19 pandemic has affected all Americans, the effects have not been equally distributed across geographic areas. These variations in the pandemic's severity-and public perceptions thereof-likely have political consequences. In this study, we examine the factors that shape perceptions of COVID-19 at the local level and assess the consequences of these perceptions for public opinion and political behaviors. METHODS: Using questions from the 2020 Cooperative Election Study (CES) linked with county-level COVID-19 rates, we examine predictors of respondents' perceptions of the severity of the pandemic in their county, including demographic, political, and informational characteristics. We also examine whether these perceptions are associated with public opinion and voter behavior. FINDINGS: Respondents' perceptions are correlated with actual case rates, although liberals and Democrats estimate the pandemic as more severe than Republicans and conservatives, as do CNN viewers compared to Fox News viewers. We found only limited evidence of a relationship between perceptions of the pandemic in a respondent's county and political outcomes. CONCLUSIONS: Results add to accumulating evidence that both news media and political predispositions shape perceptions of COVID-19, but raise important questions about whether and how the pandemic has shaped, and will continue to shape, political outcomes.

Research on Social Governance of Network Public Opinion: An Evolutionary Game Model

Compared with the past, public opinion in the we-media era has become more difficult. How to incentivize social networking providers (SNPs) to participate in network public opinion governance and guide we-media practitioners (WPs) to standardize their dissemination are prominent problems that urgently need to be solved in response to network public opinion. This article supplements the perspective of network public opinion governance research and constructs a tripartite evolutionary game model including government, SNPs, and WPs. Then, after analyzing the influencing factors of the evolution of different agents' strategies through model solving and numerical simulation, this article finds that reasonable rewards and punishments can promote SNPs to participate in network public opinion governance. Finally, this article proposes that the government should build a social governance system for network public opinion, which will effectively reduce governance costs and improve governance efficiency.

Mundane emotions: Losing yourself in boredom, time and technology

Marketing and consumer research has drawn attention to the positive and joyful emotional features of consumer tribes. However, research has little to say on boredom, an emotional state already prevalent in consumers' lives, yet exacerbated during the COVID-19 pandemic due to lockdown restrictions that prevented tribal consumption experiences. Informed by Heidegger's understanding of boredom as a fundamental mood tied to temporality, this research uses semi-structured interviews to identify two kinds of boredom – superficial and profound boredom – and their specific temporal dynamics. Superficial boredom is common and refers to a situational restlessness in which people desire distractions. In contrast, profound boredom refers to an existential discomfort in which people struggle with their sense of self, but ultimately can result in the discovery of tribal passions. We explain superficial boredom as a symptom of a dominant temporal regime that comprises connectivity and acceleration. Together these temporal logics fragment and compress time in ways that encourage mundane social media consumption that simply fills time. We also explain how profound boredom stems from an abundance of uninterrupted time spent in relative solitude. In extending Heidegger's theory of boredom to analyse contemporary boredom in an era where digital technology is ubiquitous, our research contributes to consumer research's understanding of mundane emotions and discusses what it means to be bored together.

Physical Activity & Life Satisfaction During COVID-19 Lockdown in Uzbekistan

The aim of this study is to explore the relationship between physical activity and life satisfaction in Uzbekistan during COVID-19 pandemic lockdown time. We sampled 438 respondents surveyed online during the lockdowns from July to August 2020. The empirical results obtained with the aid of OLS regression show that physical activity is significantly and positively correlated with life satisfaction. The results remain robust even when we introduce a rich set of control variables suggested by extant research.

An intermestic approach to China's public diplomacy: a case study of Beijing's COVID-19 communication in the early stages

PurposeThe aim of this study was to examine the early stages of the COVID-19 outbreak and the international communication management of Chinese diplomats as a case for extending the definition of intermestic public diplomacy. The goal was to reveal how Beijing subtly used both domestic and foreign social media to organize a network for communication about COVID-19 and purposefully soften the highly centralized and hierarchical political propaganda of the Communist Party of China (CPC).Design/methodology/approachBased on the literature on digital public diplomacy, the authors applied the existing concept of intermestic to Chinese politics in order to demonstrate the digitalization of public diplomacy, along with its forms and strategies under an authoritarian regime. A hybrid methodology combining quantitative network analysis and qualitative discourse analysis permits examination of China's intermestic online communication network dynamics, shedding light on how such an intermestic practice promoted Chinese values and power to international publics in the early stages of the COVID-19 crisis.FindingsThe authors' findings extend the implications of intermestic public diplomacy from a democratic context to an authoritarian one. By analyzing the content of public diplomacy and para-diplomatic social media accounts in China and abroad at the beginning of the COVID-19 crisis, the authors outlined China's early crisis management, explaining its intermestic public diplomacy transmission modes and strategies. Moreover, the authors identified changes in the narrative strategies of Chinese diplomats and journalists during this process.Social implicationsThe findings of this study underline that Beijing established a narrative-making virtual communication structure for disseminating favorable Chinese strategic narratives and voices through differentiated communication on domestic and foreign social media platforms. Such intermestic communication strategies were particularly evident and even further weaponized by Beijing in its large-scale Wolf Warrior diplomacy in the spring of 2020. Thus, the study's findings help readers understand how China digitalized its public diplomacy, its digital communication patterns and strategies.Originality/valueOn the one hand, geopolitical uncertainty and the popularity of social media have contributed to the evolution of the intermestic model of public diplomacy. This model allows actors to coordinate homogenous and differentiated communication practices to deploy their influence. On the other hand, the authors did not examine how intermestic audiences perceive and receive public diplomacy practices. In future studies, scholars should measure the agenda-setting capacity of diplomatic actors by examining the effects of such intermestic communication efforts.

THE POSITION OF THE GOVERNMENT AND SOCIETY OF THE CZECH REPUBLIC TOWARDS THE WAR IN UKRAINE

The aim of the article is to present the position of the Czech government and the society of this country towards the war in Ukraine. The text is an attempt to conduct a comparative analysis of the position of the Czech government and the part of society that has been opposing the government's policy towards Ukraine for some time. The author presented the main consequences of the Czech government's support for Ukraine, which are related to the post-pandemic crisis. These include, above all, problems with the supply of oil, natural gas and nuclear fuel for Czech nuclear power plants. Social problems include high inflation, rising interest rates and rising prices ofbasic commodities. In the summary, synthetic conclusions were drawn that confirm the discrepancies between the government's policy and the position of that part of society.