Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

Exploring the exponential sensitivity of risk perception in the COVID-19 pandemic

Individual's risk perception regarding specific hazards is a dynamic process that evolves over time. This study analyzed the relationship between the number of COVID-19 cases and the South Korean public's risk perceptions from the outset of the pandemic to the recent past. More than 70 repeated cross-sectional surveys were conducted biweekly to measure individuals' risk perception. An autoregressive integrated moving average with explanatory variable time series analysis was used to characterize the relationship between the number of COVID-19 cases and level of risk perceptions. It revealed that individuals' risk perception and the number of COVID-19 cases were not linearly related but were logarithmically correlated. This finding can be understood as a psychic numbing effect, suggesting that people's perception of risk is not linear but rather exponentially sensitive to changes. The findings also revealed a significant influence of individuals' trust in local governments on their risk perceptions, highlighting the substantial role played by local governments in direct risk management during the COVID-19 pandemic.

Work-life balance Twitter insights: A social media analysis before and after COVID-19 pandemic

This research examines the perceptions of Twitter users regarding the prevalent topics within Work-Life Balance communication before and after the COVID-19 pandemic. The pressing questions surrounding current labour market drivers are addressed, particularly regarding the ongoing Fourth Industrial Revolution and the COVID-19 pandemic's impact on communicated themes, particularly in the Human Resource Management field, where Work-Life Balance has emerged as a key concept. Social media platforms like Twitter are pivotal in fostering discussions on Work-Life Balance in society. Over the past decade, Twitter has evolved into a significant research platform researchers utilise in more than ten thousand research articles. The online discourse on Twitter raises awareness of the importance of balancing work and personal life. The COVID-19 pandemic has unveiled new facets of Work-Life Balance, with social media as a key platform for discussing these issues. This research uses Social Media Analysis based on the Hashtag Research framework. A total of 1,768,628 tweets from 499,574 users were examined, and frequency, topic, and sentiment analysis were conducted. Pre-pandemic, the most communicated Work-Life Balance topics were performance and time management, while recruitment and employee development were identified post-pandemic. Pre-pandemic, the highest proportion of negative sentiment was time management and mental health prevention, shifting to time, employee development, and mental health prevention post-pandemic. Despite the limitations of our research, a proposed redefinition of the concept is also presented, including a design for an integrated Work-Life Balance model based on topics communicated by Twitter users. Given the need for a more robust approach to redefining the concept and developing an integrative Work-Life Balance model, the article provides fresh insights for future research.

Cross-sectional study: mindset towards Coronavirus in developing countries and lessons learned: the case of Sudan

Introduction

People's mindset towards COVID-19 in developing countries has an impact on how they perceive and react to the preventative measures taken by the governments to contain the virus. Understanding the factors influencing the mindset and identifying lessons learned amidst COVID-19 are critical to inform any future intervention strategy.

Methods

This was a cross-sectional, community-based study conducted to assess the mindset changes and lessons learned post-COVID-19 in developing countries, focusing on Sudan. The study adopted a sequential mixed approach (SMA), combining qualitative and quantitative methods. The study used a structured questionnaire with 300 respondents and in-depth interviews with two experts. To identify the factors influencing the mindset of the people towards COVID-19, the study employed logistic regression. The data was analyzed using SPSS software.

Results

Of the total (N = 300) respondents, 59.0% are female, 59.3% are between the ages of 20 and 39, 79.7% have a university education, 25.3% have the Coronavirus, and 42.3% has their family or relative contracted the virus. Further, only 22.7% had taken the vaccine. Reasons for vaccine hesitancy include lack of trust (29.5%), fear of side effects (24.1%), and absence of the need to travel outside the country (25.5%). When the virus first appeared, 77.3% thought it posed a health risk, while 22.7% perceived it as a hoax or conspiracy. After 3 years, 73% still regarded it as a health threat, while 27% believed it was a hoax or conspiracy. The mindset was found to be influenced by age, history of the disease, the extent of trust in foreign media coverage, and the belief in the effectiveness of the vaccination.

Conclusion

Assessing the mindset towards the virus and identifying the lessons learned from the pandemic could be of vital importance to control the spread of the virus in developing countries. Making use of such lessons and influencing the mindset of the people towards positive attitudes and behaviours are required to enhance the effectiveness of the health precaution measures adopted. Further research is required on the public's mistrust of foreign media coverage and the contribution of local media to educate the public about the virus, particularly among the elderly.

Crisis Communication during COVID-19: English, French, Portuguese, and Spanish Discourse of AstraZeneca Vaccine and Omicron Variant on Social Media

Social media have been the arena of different types of discourse during the COVID-19 pandemic. We aim to characterize public discourse during health crises in different international communities. Using Tweetpy and keywords related to the research, we collected 3,748,302 posts from the English, French, Portuguese, and Spanish Twitter communities related to two crises during the pandemic: (a) the AstraZeneca COVID-19 vaccine, and (b) the Omicron variant. In relation to AstraZeneca, 'blood clot' was the main focus of public discourse. Using quantitative classifications and natural language processing algorithms, results are obtained for each language. The English and French discourse focused more on "death", and the most negative sentiment was generated by the French community. The Portuguese discourse was the only one to make a direct reference to a politician, the former Brazilian President Bolsonaro. In the Omicron crisis, the public discourse mainly focused on infection cases follow-up and the number of deaths, showing a closer public discourse to the actual risk. The public discourse during health crises might lead to different behaviours. While public discourse on AstraZeneca might contribute as a barrier for preventive measures by increasing vaccine hesitancy, the Omicron discourse could lead to more preventive behaviours by the public, such as the use of masks. This paper broadens the scope of crisis communication by revealing social media's role in the constructs of public discourse.

Global analysis of Twitter communication in corporate social responsibility area: sustainability, climate change, and waste management

Many people now consider social media to be an integral part of their daily routines, which has enabled companies to implement successful corporate social responsibility campaigns through these platforms. The direct interaction with stakeholders offered by social media helps companies to build understanding, trust, and their image. The aim of this study was to identify key topics and trends communicated in connection with corporate social responsibility on the Twitter social network from 2017 to 2022. Analysis of 520,638 tweets by 168,134 unique users identified a predominance of environment-related topics: Sustainability, Climate Change, and Waste management. However, Charity remains the largest single topic. Based on the trend analysis, the areas of ESG, Social Impact, and Charity were identified as growth areas in communication, while Green and Philanthropy, on the other hand, were identified as decreasing.

Emotions and virality: Social transmission of political messages on Twitter

Drawing on previous literature that valence and arousal constitute the fundamental properties of emotions and that emotional content is a determinant of social transmission, this study examines the role of valence and arousal in the social transmission of politicians' messages on Twitter. For over 3,000 tweets from five Austrian party leaders, the discrete emotion that the message intended to elicit in its recipients was captured by human coders and then classified on its valence (positive or negative) and arousal (low or high). We examined the effects of valence and arousal on the retweet probability of messages. Results indicate that tweets eliciting a negative (vs. positive) valence decreased retweet probability, whereas tweets eliciting a high (vs. low) arousal increased retweet probability. The present research replicates previous findings that arousal constitutes a determinant of social transmission but extends this mechanism to the realm of political communication on Twitter. Moreover, in contrast to the frequently mentioned negativity bias, positive emotions increased the likelihood of a message being shared in this study.

Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach

Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information.

[Delta of neutrophil lymphocyte index and mortality in covid-19 disease]

Background

Acute respiratory distress syndrome, due to SARS-CoV-2, is a worldwide health problem. The neutrophil-lymphocyte index allows risk stratification in patients with severe and poor prognostic data, since it reflects the inflammatory state.

Objective

To determine whether the Neutrophil-Lymphocyte Index delta predicts mortality in patients with COVID-19.

Material and methods

We conducted a longitudinal, comparative study in patients with COVID-19, older than 18 years, admitted to the ICU. We evaluated HAS, DM, obesity, COPD, asthma, PaO2/FiO2, tomographic severity. On admission and on days 3 and 7 we measured Neutrophil-Lymphocyte Index, SOFA and APACHE score. For statistical analysis, we performed ROC and Kaplan-Meyer curves.

Results

We included 180 patients with COVID-19, 63 died (35%). Delta INL1(Day1-day3)>4.11 was associated with mortality (AUC:0.633); sensitivity 55.56% and specificity 77.78%, CI95 0.55-0.70, for delta INL2 (Day1-day7)>8.95 (AUC:0.623); sensitivity 44.44% and specificity 84.62%, CI95 0.54-0.69. Difference in survival was observed for Delta1. SOFA scale >6, was associated with more days of mechanical ventilation and lower PaO2/FiO2 (p<0.001).

Conclusions

INL delta between the day of ICU admission and the 3rd day of evolution is a predictor of mortality in critically ill patients.

Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period

Despite the demonstrated efficacy, safety, and availability of COVID-19 vaccines, efforts in global mass vaccination have been met with widespread scepticism and vaccine hesitancy or refusal. Understanding the reasons for the public's negative opinions towards COVID-19 vaccination using Twitter may help make new headways in improving vaccine uptake. This study, therefore, examined the prevailing negative sentiments towards COVID-19 vaccination via the analysis of public twitter posts over a 16 month period. Original tweets (in English) from 1 April 2021 to 1 August 2022 were extracted. A bidirectional encoder representations from transformers (BERT)-based model was applied, and only negative sentiments tweets were selected. Topic modelling was used, followed by manual thematic analysis performed iteratively by the study investigators, with independent reviews of the topic labels and themes. A total of 4,448,314 tweets were analysed. The analysis generated six topics and three themes related to the prevailing negative sentiments towards COVID-19 vaccination. The themes could be broadly understood as either emotional reactions to perceived invidious policies or safety and effectiveness concerns related to the COVID-19 vaccines. The themes uncovered in the present infodemiology study fit well into the increasing vaccination model, and they highlight important public conversations to be had and potential avenues for future policy intervention and campaign efforts.

Exploring Trends in Environmental, Social, and Governance Themes and Their Sentimental Value Over Time

Environmental, social, and governance (ESG) is an indicator that measures a company's non-financial performance. Many firms have recently emphasized the importance of ESG. Ascertaining what topics are being discussed around ESG and how they change over time will contribute significantly to gaining insight into ESG. Using 73,397,870 text data scraped and refined from publicly available Twitter data, this study applied Latent Dirichlet Allocation (LDA) and the dynamic topic model (DTM) to ascertain the hidden structure of the ESG-related document collection and the topics being discussed. The study further conducts a sentiment analysis to examine the sentiment of the general public regarding ESG. Topic modeling shows that various topics regarding ESG are being discussed and evolve over time. Sentiment analysis shows that many people have neutral or positive sentiments toward ESG-related issues. This study contributes to exploring insights into ESG among the public and understanding public reactions toward ESG. We further conclude the study with a discussion of managerial implications and potential future research.