Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India

Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S

Social networks, particularly Twitter 9.0 (known as X as of 23 July 2023), have provided an avenue for prompt interactions and sharing public health-related concerns and emotions, especially during the COVID-19 pandemic when in-person communication became less feasible due to stay-at-home policies in the United States (U.S.). The study of public emotions extracted from social network data has garnered increasing attention among scholars due to its significant predictive value for public behaviors and opinions. However, few studies have explored the associations between public health policies, local political ideology, and the spatial-temporal trends of emotions extracted from social networks. This study aims to investigate (1) the spatial-temporal clustering trends (or spillover effects) of negative emotions related to COVID-19; and (2) the association relationships between public health policies such as stay-at-home policies, political ideology, and the negative emotions related to COVID-19. This study employs multiple statistical methods (zero-inflated Poisson (ZIP) regression, random-effects model, and spatial autoregression (SAR) model) to examine relationships at the county level by using the data merged from multiple sources, mainly including Twitter 9.0, Johns Hopkins, and the U.S. Census Bureau. We find that negative emotions related to COVID-19 extracted from Twitter 9.0 exhibit spillover effects, with counties implementing stay-at-home policies or leaning predominantly Democratic showing higher levels of observed negative emotions related to COVID-19. These findings highlight the impact of public health policies and political polarization on spatial-temporal public emotions exhibited in social media. Scholars and policymakers can benefit from understanding how public policies and political ideology impact public emotions to inform and enhance their communication strategies and intervention design during public health crises such as the COVID-19 pandemic.

Physical activity and mental health during the COVID-19 pandemic among individuals with amputation

The isolating nature of various COVID-19 mandates may have reduced physical activity (PA) and increased mental health symptomology among individuals with amputation. However, an investigation of mental health across PA levels before and after the onset of COVID-19 among this group has not been conducted. Therefore, the objective of this study was to investigate group differences in depression, anxiety, and post-traumatic stress symptomology among individuals with amputation who reported being physically "active," "somewhat active," or "inactivate" before and during the pandemic. Individuals with an amputation at any level (n = 91; 51% female; age = 52.5±15.5) completed an online questionnaire to assess demographic information, PA levels, and mental health throughout the pandemic. Group differences in self-reported PA before and after COVID-19 onset were assessed by the PA Guidelines for Americans recommendations. The Center for Epidemiologic Studies Depression Scale (CES-D), Generalized Anxiety Disorder (GAD-7), and Posttraumatic Stress Disorder Checklist (PCL-5) scales were used to assess group differences in mental health status. Before and after the onset of COVID-19, 33% and 42.9% of respondents reported that they were inactive, respectively. 58.2% of respondents reported decreased PA since the pandemic's onset. Prior to the pandemic, active individuals reported lower CES-D (14.21 vs. 19.07; Cohen's d: -0.414), GAD-7 (3.82 vs. 5.47; Cohen's d: -0.359), and PCL-5 (15.92 vs. 21.03; Cohen's d: -0.319) scores compared to inactive individuals. After the onset of COVID-19, scores remained lower for active respondents CES-D (12.67 vs. 20.03; Cohen's d: 0.-669), GAD-7 (3.17 vs. 5.87; Cohen's d: -0.598), and PCL-5 (13.39 vs. 19.90; Cohen's d: -0.430). Individuals with amputation reported decreased PA after the onset of COVID-19. Individuals reporting that they were "active" exhibited improved depression and anxiety symptomology scores compared to those reporting that they were "inactive."

COVID-19 case prediction using emotion trends via Twitter emoji analysis: A case study in Japan

Introduction

The worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward COVID-19 expressed on social media, specifically Twitter, can be used to enhance COVID-19 case prediction system performance.

Methods

We use emoji as a proxy to shallowly capture the trend in emotion expression on Twitter. Two aspects of emoji are studied: the surface trend in emoji usage by using the tweet count and the structural interaction of emoji by using an anomalous score.

Results

Our experimental results show that utilizing emoji improved system performance in the majority of evaluations.

A Study on the Relationship between Internet Overdependence and Anger Response among Young Adults during COVID-19 Pandemic: Moderating Effect on Negative Emotions

The aim of this study is to examine how Internet dependence affects anger responses during the COVID-19 pandemic. Owing to social distancing policies, Internet dependence has intensified, and the prevalence of anger has significantly increased. To understand this phenomenon and draw some implications, the "frustration-aggression hypothesis" was utilized for the theoretical framework and anger response was categorized into functional and dysfunctional anger responses. An analysis shows that overdependence on the Internet has a positive effect on the dysfunctional anger response. At the same time, other negative emotions replace anger, reducing the possibility of a dysfunctional anger response. Accordingly, a need for a constant effort to understand the circumstances of the young generation living in the "new normal" is emphasized; moreover, this paper suggests some theoretical and practical implications.

Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study

Background

Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online.

Objective

This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia.

Methods

We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks.

Results

Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the "vaccine_rollout" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases.

Conclusions

By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.

Public perceptions of digital fashion: An analysis of sentiment and Latent Dirichlet Allocation topic modeling

Since digital technology has had a significant impact on the fashion industry, digital fashion has become a hot topic in today's society. Currently, research on digital fashion is focused on the transformation of enterprise marketing strategies and the discussion of digital technology. Despite this, the current study does not include an analysis of the audience's emotional and cognitive responses to digital fashion on social networking platforms. A comprehensive analysis and discussion of 52,891 posts about digital fashion and virtual fashion published on social networking sites was conducted using k-means clustering analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis in this study. The study examines the public's perception and hot topics about digital fashion, as well as the industry's development situation and trends. According to the findings, both positive and neutral emotions accompany the public's attitude toward digital fashion. There is a wide range of topics covered in the discussion. Innovations in digital technology have impacted the creation of jobs, talent demand, marketing strategies, profit forms, and industrial chain innovation of fashion-related businesses. Researchers in related fields will find this study useful not only as a reference for research methods and directions, but also as a source of references for research methodology. A case study and data reference will also be provided to industry practitioners.

The antidepressant effect of physical exercise: Evidence from China Family Panel Studies

Multiple studies have proved that participating in sports can effectively reduce adults’ depression. This paper provides evidence from China by using the survey data from China Family Panel Studies (CFPS), which contains sport-types, personal characteristics, and CES-D20 depression-scale score data of 33,236 individuals. In addition to the Ordinary Least Squares regression model, we adopt the Two-way Fixed Effect and Propensity Score Matching method to alleviate the endogeneity. The empirical result shows that for every additional time of physical exercise, the depression level drops by an average of 0.152; the depression level of people who participate in sports is significantly lower than that of non-participants by 0.397 points. The lowering effect of physical activity on depression is not linear, and excessive exercise may lead to increased depression. Furthermore, heterogeneity analyses discover that with the increase of age and education, the impact continued to expand. For every increase in physical exercise of the group over 76-year old, the depression level decreased by 0.373 points; while for individuals with primary school education, their depression level decreased only by 0.124 points.

Effectiveness of online mindfulness-based interventions in improving mental health during the COVID-19 pandemic: A systematic review and meta-analysis of randomized controlled trials

Introduction

Psychotherapies, such as mindfulness-based interventions (MBIs), are currently needed to tackle mental health problems. Online MBIs have become promising since face-to-face interventions are limited during the COVID-19 pandemic due to lockdown and social distancing. This systematic review and meta-analysis aimed to investigate the effect of online MBIs in improving mental health, mainly depression, anxiety, and stress.

Materials and methods

A systematic literature search was conducted according to the PRISMA 2020 guidelines on several databases for eligible studies up to October 17, 2021. Study quality was assessed using the Cochrane’s Risk of Bias 2 tool. Effect sizes were presented as standardized mean difference (Hedges’ g) between the online MBIs and control groups at post-test and follow-up using a random-effects model.

Results

Eight randomized controlled trials involving 868 participants were included in this meta-analysis. The pooled adherence rate to online MBIs was 94% (95% CI = 91% to 98%). The findings revealed that online MBIs had a statistically significant small to moderate effect in reducing depression (g = -0.32; 95% CI = -0.49 to -0.14; I² = 0%), a small effect on anxiety (g = -0.25; 95% CI = -0.43 to -0.06; I² = 27%), and a moderate effect on stress (g = -0.62; 95% CI = -1.09 to -0.16; I² = 83%). In addition, significant small effects at follow-up were observed for depression (g = -0.26; 95% CI = -0.48 to -0.04; I² = 0%) and anxiety (g = -0.28; 95% CI = -0.48 to -0.08; I² = 0%), but not for stress.

Conclusion

Online MBIs have beneficial effects on mental health, particularly depression, anxiety, and stress, during the COVID-19 pandemic. Given the limitations of the current study, future trials that specifically consider potential effect influencing factors, longer follow-up evaluation, and methodological quality are warranted.

Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media.

Tweet Analysis for Enhancement of COVID-19 Epidemic Simulation: A Case Study in Japan

The COVID-19 pandemic, which began in December 2019, progressed in a complicated manner and thus caused problems worldwide. Seeking clues to the reasons for the complicated progression is necessary but challenging in the fight against the pandemic. We sought clues by investigating the relationship between reactions on social media and the COVID-19 epidemic in Japan. Twitter was selected as the social media platform for study because it has a large user base in Japan and because it quickly propagates short topic-focused messages ("tweets"). Analysis using Japanese Twitter data suggested that reactions on social media and the progression of the COVID-19 epidemic may have a close relationship. Analysis of the data for the past waves of COVID-19 in Japan revealed that the relevant reactions on Twitter and COVID-19 progression are related repetitive phenomena. We propose using observations of the reaction trend represented by tweet counts and the trend of COVID-19 epidemic progression in Japan and a deep neural network model to capture the relationship between social reactions and COVID-19 progression and to predict the future trend of COVID-19 progression. This trend prediction would then be used to set up a susceptible-exposed-infected-recovered model for simulating potential future COVID-19 cases. Experiments to evaluate the potential of using tweets to support the prediction of how an epidemic will progress demonstrated the value of using epidemic-related social media data. Our findings provide insights into the relationship between user reactions on social media, particularly Twitter, and epidemic progression, which can be used to fight pandemics.

Twitter Sentiment Analysis Using Ensemble based Deep Learning Model towards COVID-19 in India and European Countries

As of November 2021, more than 24.80 crore people are diagnosed with the coronavirus in that around 50.20 lakhs people lost their lives, because of this infectious disease. By understanding the people's sentiment's expressed in their social media (Facebook, Twitter, Instagram etc.) helps their governments in controlling, monitoring, and eradicating the coronavirus. Compared to other social media's, the twitter data are indispensable in the extraction of useful awareness information related to any crisis. In this article, a sentiment analysis model is proposed to analyze the real time tweets, which are related to coronavirus. Initially, around 3100 Indian and European people's tweets are collected between the time period of 23.03.2020 to 01.11.2021. Next, the data pre-processing and exploratory investigation are accomplished for better understanding of the collected data. Further, the feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF), GloVe, pre-trained Word2Vec, and fast text embedding's. The obtained feature vectors are fed to the ensemble classifier (Gated Recurrent Unit (GRU) and Capsule Neural Network (CapsNet)) for classifying the user's sentiment's as anger, sad, joy, and fear. The obtained experimental outcomes showed that the proposed model achieved 97.28% and 95.20% of prediction accuracy in classifying the both Indian and European people's sentiments.

Effects of COVID-19 on Japanese medical students' knowledge and attitudes toward e-learning in relation to performance on achievement tests

The COVID-19 pandemic forced many educational institutions to turn to electronic learning to allow education to continue under the stay-at-home orders/requests that were commonly instituted in early 2020. In this cross-sectional study, we evaluated the effects of the COVID-19 pandemic on medical education in terms of students' attitudes toward online classes and their online accessibility; additionally, we examined the impacts of any disruption caused by the pandemic on achievement test performance based on the test results. The participants were 674 students (412 in pre-clinical, 262 in clinical) at Juntendo University Faculty of Medicine; descriptive analysis was used to examine the respondents' characteristics and responses. The majority of respondents (54.2%) preferred asynchronous classes. Mann-Whitney U tests revealed that while pre-clinical students preferred asynchronous classes significantly more than clinical students (39.6%, p < .001), students who preferred face-to-face classes had significantly higher total achievement test scores (U = 1082, p = .021, r = .22). To examine the impacts of pandemic-induced changes in learning, we conducted Kruskal-Wallis tests and found that the 2020 and 2021 scores were significantly higher than those over the last three years. These results suggest that while medical students may have experienced challenges adapting to electronic learning, the impact of this means of study on their performance on achievement tests was relatively low. Our study found that if possible, face-to-face classes are preferable in an electronic learning environment. However, the benefit of asynchronous classes, such as those that allow multiple viewings, should continue to be recognized even after the pandemic.

Tracing changes in teachers' professional learning network on Twitter: Comparison of teachers' social network structure and content of interaction before and during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic has affected teaching and professional learning activities. Teachers may have to rely on online spaces, such as Twitter, to interact with their professional learning networks and get enough support.

OBJECTIVES

This study aims to investigate the structure and the content of teachers' network interactions on Twitter both before and during the COVID19 pandemic.

METHODS

Data were analysed for 103 teachers using multiple methods, including social network analysis (SNA) and content and thematic analysis.

RESULTS AND CONCLUSIONS

Content analysis revealed that teachers' cognitive and affective posts increased significantly during COVID-19. Thematic analysis showed that, during COVID-19, teachers' post sfocused on issues around digital transformation. SNA showed that the sizes of teachers' networks and in/out-ties grew during COVID-19. Although the study finds that teachers interacted with individuals both within and outside their discipline and their country, most teachers' interactions were with teachers from similar disciplines and same country. Teachers used Twitter to share information and support each other.

IMPLICATIONS

This study provides recommendations for stimulating professional interactions among teachers. This work shows the potential of SNA and content analysis to analyse teachers' professional learning networks.

Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter

Social networks have created an information diffusion corpus that provides users with an environment where they can express their views, form a community, and discuss topics of similar or dissimilar interests. Even though there has been an increasingly rising demand for conducting an emotional analysis of the users on social media platforms, the field of emotional intelligence (EI) has been rather slow in exploiting the enormous potential that social media can play in the research and practice of the framework. This study, thus, tried to examine the role that the microblogging platform Twitter plays in enhancing the understanding of the EI community by building on the Twitter Analytics framework of Natural Language Processing to further develop the insights of EI research and practice. An analysis was conducted on 53,361 tweets extracted using the hashtag emotional intelligence through descriptive analytics (DA), content analytics (CA), and network analytics (NA). The findings indicated that emotional intelligence tweets are used mostly by speakers, psychologists (or other medical professionals), and business organizations, among others. They use it for information dissemination, communication with stakeholders, and hiring. These tweets carry strong positive sentiments and sparse connectedness. The findings present insights into the use of social media for understanding emotional intelligence.

Social Connectivity, Sentiment and Participation on Twitter during COVID-19

In a transnational context defined by the irruption of COVID-19 and the social isolation it has generated around the world, social networking sites are essential channels for communicating and developing new forms of social coexistence based on connectivity and interaction. This study analyzes the feelings expressed on Twitter through the hashtags #YoMeQuedoEnCasa, #stayhome, #jeresteàlamaison, #restealamaison, #stoacasa, #restaacasa, #ficaemcasa, #euficoemcasa, #ichbleibezuHause and #Bleibzuhause, and the communicative and social processes articulated from network participation, during the lockdown in 2020. Through Gephi software, the aspects underlying the communicative interaction and the distribution of the network at a global level are studied, with the identification of leaderships, communities and connectivity nodes. As a result of this interaction, the emergence of social and organizational links derived from community participation and motivated by the common interest of preserving health and general wellbeing through collective action is detected. The study notes the presence of feelings of solidarity, a sense of community and social support among connected crowds who, despite being in geographically dispersed settings, share similar concerns about the virus effect.

Factors Influencing Students’ Behavior and Attitude towards Online Education during COVID-19

Universities around the world have faced a new pandemic, forcing the closure of campuses that are now conducting educational activities on online platforms. The paper presents a survey about students behavior and attitudes towards online education in the pandemic period from the Technical University of Cluj Napoca, Romania. A group of 300 students participated. The questionnaire was structured in four parts to determine student’s individual characteristics, student’s needs, students’ knowledge in using virtual platforms and students’ quality preferences for online education. The students said that online education in a pandemic situation is beneficial for 78% of them. A total of 41.7% percent of students appreciated the teachers’ teaching skills and the quality of online courses since the beginning of the pandemic, and 18.7% percent of the students appreciated the additional online materials for study to support their education. However, students found online education stressful, but preferred online assessment for evaluation. This pandemic has led to the new stage of Education 4.0, online education, and the need to harmonize methods of education with the requirements of new generations.

Introduction to the Special Edition on Social Media

This special issue of the Journal for Technology in Behavioral Science includes articles focused on some of the diverse uses and issues related to social media use by the public, clients and patients, and health care professionals. Social media broadly includes many forms of electronic communications other than Facebook and Twitter, is continuously evolving, and for many a frequent form of communicating with others. Potential benefits and risks of using social media are identified with no clear consensus on many of the issues it presents.