Social TV viewing during the COVID-19 lockdown: The mediating role of social presence

The COVID-19 lockdown has transformed the way of life for many people. One key change is media intake, as many individuals reported an increase in media consumption during the COVID-19 lockdown. Specifically, social media and television usage increased. In this regard, the present study examines social TV viewing, the simultaneous use of watching TV while communicating with others about the TV content on various communication technologies, during the COVID-19 lockdown. An online survey was conducted to collect data from college students in the United States during the COVID-19 lockdown. Primary results indicate that different motives predict different uses of communication platforms for social TV engagement, such as public platforms, text-based private platforms, and video-based private platforms. Specifically, the social motive significantly predicts social TV engagement on most of the platforms. Further, the study finds that social presence of virtual co-viewers mediates the relationship between social TV engagement and social TV enjoyment. Overall, the study's findings provide a meaningful understanding of social TV viewing when physical social gatherings are restricted.     

Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.     

Effects of excessive use of mobile phone technology in India on human health during COVID-19 lockdown

ObjectiveThe global health crisis in the form of COVID-19 has forced people to shift their routine activities into a remote environment with the help of technology. The outbreak of the COVID-19 has caused several organizations to be shut down and forced them to initiate work from home employing technology. Now more than ever, it's important for people and institutions to understand the impact of excessive use of mobile phone technology and electronic gadgets on human health, cognition, and behavior. It is important to understand their perspective and how individuals are coping with this challenge in the wake of the COVID-19 pandemic. The investigation is an effort to answer the research question: whether dependency on technology during lockdown has more effects on human health in comparison to normal times.MethodsThe study included participants from India (n = 122). A questionnaire was framed and the mode of conducting the survey chosen was online to maintain social distancing during the time of the Pandemic. The gathered data was statistically analysed employing RStudio and multiple regression techniques.ResultsThe statistical analysis confirms that lockdown scenarios have led to an increase in the usage of mobile phone technology which has been confirmed by around 90% of participants. Moreover, 95% of the participants perceive an increased risk of developing certain health problems due to excessive usage of mobile phones and technology. It has been evaluated that participants under the age group 15–30 years are highly affected (45.9%) during lockdown due to excessive dependence on technology. And, amongst different professions, participants involved in online teaching-learning are the most affected (42.6%).ConclusionThe findings indicate that dependency on technology during lockdown has more health effects as compared to normal times. So, it is suggested that as more waves of pandemics are being predicted, strategies should be planned to decrease the psychological and physiological effects of the overuse of technology during lockdown due to pandemics. As the lockdown situation unfolds, people and organization functioning styles should be rolled back to the limited dependency on technology.     

Health vs. privacy? The risk-risk tradeoff in using COVID-19 contact-tracing apps

In the midst of the COVID-19 pandemic, contact-tracing apps have emerged as reliable tools for public health communication and the promotion of preventative health. However, to function properly, contact-tracing apps require users to provide sensitive information, which has raised concerns about data disclosure, misuse and social surveillance. Little is known about how different types of risk perception simultaneously hinder and motivate individuals' engagement in mobile health apps, particularly in the context of a pandemic. Based on the privacy calculus theory and the risk-risk tradeoff concept, this study examined the risk-risk tradeoff model to enhance the understanding of COVID-19 contact-tracing app users’ decision from the perspective of risk minimization. Findings from PLS-SEM and fsQCA revealed that users engage in health risk-privacy risk tradeoff when evaluating and deciding to use the apps. The focal study therefore contributes to the research on privacy calculus theory and calls for a balanced managerial solution to mitigate this tradeoff dilemma.     

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R² values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.     

The impact of a 360° virtual tour on the reduction of psychological stress caused by COVID-19

The continuous spread of the COVID-19 pandemic is causing people to feel anxiety and stress. This study constructs a four-layer research model to examine how a 360° virtual tour can reduce people's psychological stress through two types of presence (the sense of presence and telepresence) and affective-motivational states (enjoyment and involvement) in this extraordinary period of the COVID-19 pandemic. In order to test the moderating effect of involvement, partial least squares (PLS) analysis is employed to analyse valid data collected from 235 individuals. The results of this study indicate that telepresence has a higher impact in generating affective-motivational states than the sense of presence. Among the factors, enjoyment shows the highest effect on satisfaction with the 360° virtual tour experience and stress reduction; involvement moderates the effect of telepresence on satisfaction with the 360° virtual tour experience. This study also contributes to virtual reality research by distinguishing the concepts of ‘sense of presence’ and ‘telepresence’ as well as demonstrating the mechanisms whereby virtual reality technology influences people's psychological well-being. Timely recommendations are provided for people in order to reduce psychological stress during and after COVID-19 pandemic.     

Privacy and surveillance attitudes during health crises: Acceptance of surveillance and privacy protection behaviours

The wide deployment of digital technologies for the management of the COVID-19 pandemic has triggered concerns about privacy and intrusion from government surveillance. This study investigates individual privacy and surveillance attitudes by developing a theoretical model to explain acceptance of government surveillance and privacy protection behaviours during health-crises, such as the COVID-19 pandemic. Results from a US sample reveal that people are concerned about the collection and use of their personal information via mobile applications and the monitoring of their online activities by authorities. Findings reveal the important roles of political trust and belief that governments' need to be proactive in protecting peoples’ welfare during a crisis that can increase acceptance of surveillance and thus assist in the management of the health crisis. Implications for research and practice are discussed.     

YouTube's growth in use among children 0–5 during COVID19: The Occidental European case

YouTube has become an educational and entertainment tool among Western European families, particularly during the COVID-19 pandemic. This study monitored the main channels for children aged 0–5 years by using the social media analysis (SNA) methodology from March 24, 2020 to August 24, 2020. The software used has been FanpageKarma, which allows the collection and interpretation of data. The results indicate not only a growth in the use of such channels during confinement, but also how their expansion is related to the evolution of the COVID-19, reflecting, in turn, the consequences of the government policies adopted. Social distancing generated a greater consumption of recreational content, but not a greater growth in educational content regardless of the country or culture.     

Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets. The pandemic has shoved the entire planet into an unstable state. The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia. To achieve this objective, the study analyzes two perspectives: the early approach, and the late approach of COVID-19 and the consequent effects on different aspects of the society. We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society. Findings of this research study indicate that financial resources were the worst affected. Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people. Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.     

Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia

With increasing frequency, people are using social media sites to obtain timely information about the world's grand challenges and this phenomenon is amplified during crises. However, little research has been conducted to determine how people participate and how their involvement can be promoted on social media sites, although the critical role played by those sites has been well documented. Based on the theory of planned behavior (TPB), this study develops and tests a theoretical model to establish the effect of several factors with survey data collected during the COVID-19 pandemic, in Saudi Arabia. The relationship was verified on a sample of 213 respondents active on Twitter, using Partial Least Square (PLS). The study found that attitude, perceived behavioural control and subjective norm affect Twitter users' active participation significantly within the context of a time of crisis. It also found a positive effect of utilitarian and hedonic values and trust. These results will provide a more comprehensive evaluation of Twitter users in grand challenges (and more specifically during a crisis) and furnish academics and managers with instructive guidance.     

Which features of an outpatient treatment for COVID-19 would be most important for pandemic control? A modelling study

The global pandemic of coronavirus disease 2019 (COVID-19) has challenged healthcare systems worldwide. Lockdown, social distancing, and screening are thought to be the best means of stopping the virus from spreading and thus of preventing hospital capacity from being overloaded. However, it has also been suggested that effective outpatient treatment can control pandemics. We adapted a mathematical model of the beneficial effect of lockdown on viral transmission and used it to determine which characteristics of outpatient treatment would stop an epidemic. The data on confirmed cases, recovered cases, and deaths were collected from Santé Publique France. After defining components of the epidemic flow, we used a Morris global sensitivity analysis with a 10-level grid and 1000 trajectories to determine which of the treatment parameters had the largest effect. Treatment effectiveness was defined as a reduction in the patients'' contagiousness. Early treatment initiation was associated with better disease control—as long as the treatment was highly effective. However, initiation of a treatment with a moderate effectiveness rate (5%) after the peak of the epidemic was still better than poor distancing (i.e. when compliance with social distancing rules was below 60%). Even though most of today''s COVID-19 research is focused on inpatient treatment and vaccines, our results emphasize the potentially beneficial impact of even a moderately effective outpatient treatment on the current pandemic.     

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus infection. Twitter has become one of the popular social media platforms and allows anyone to post tweets. This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based sentiment analysis (SCOBGRU-SA) on COVID-19 tweets. The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic. The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this. Moreover, the BGRU model is utilized to recognise and classify sentiments present in the tweets. Furthermore, the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter, which helps attain improved classification performance. The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset, and the results signify its promising performance compared to other DL models.     

What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments. We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler, including texts and images posted by users. In order to effectively discover what is being discussed about COVID-19 without human labeling, we propose a unified multi-modal framework, including an unsupervised short-text topic model to discover and track bursty topics, and a selfsupervised model to learn image features so that we can retrieve related images about COVID-19. Experimental results have shown the effectiveness and superiority of the proposed models, and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.     

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved.     

The state of social science research on COVID-19

Research on COVID-19 has proliferated rapidly since the outbreak of the pandemic at the end of 2019. Many articles have aimed to provide insight into this fast-growing theme. The social sciences have also put effort into research on problems related to COVID-19, with numerous documents having been published. Some studies have evaluated the growth of scientific literature on COVID-19 based on scientometric analysis, but most of these analyses focused on medical research while ignoring social science research on COVID-19. This is the first scientometric study of the performance of social science research on COVID-19. It provides insight into the landscape, the research fields, and international collaboration in this domain. Data obtained from SSCI on the Web of Science platform was analyzed using VOSviewer. The overall performance of the documents was described, and then keyword co-occurrence and co-authorship networks were visualized. The six main research fields with highly active topics were confirmed by analysis and visualization. Mental health and psychology were clearly shown to be the focus of most social science research related to COVID-19. The USA made the most contributions, with the most extensive collaborations globally, with Harvard University as the leading institution. Collaborations throughout the world were strongly related to geographical location. Considering the social impact of the COVID-19 pandemic, this scientometric study is significant for identifying the growth of literature in the social sciences and can help researchers within this field gain quantitative insights into the development of research on COVID-19. The results are useful for finding potential collaborators and for identifying the frontier and gaps in social science research on COVID-19 to shape future studies.

     

The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners’ benefits.     

Aerial Bots in the Supply Chain: A New Ally to Combat COVID-19

The rapid global spread of COVID-19 has caused disruptions in various supply chains and people's lives. At the same time, it has paved the way for drone technology (Aerial bots). With the countries gone into lockdown for an unspecified time, it is self-evident that people will run out of food, medicine, and other essentials because of the middleman's unavailability to move products from supply to demand point. Lack of medical infrastructure and distant testing laboratories is another challenge faced by the countries, which result in a delayed testing report leading to delay in medical treatment—such critical problems arising in the fight against COVID-19 highlight the need for improving the efficiency of supply chains. Recently used for commercial purposes, drone technology has already proved its utility in inventory and logistics management. Therefore, we argue that drones could be a viable option to improve the efficiency and effectiveness of the supply chains working for humanitarian aid to combat COVID-19. Specifically, the focus is on food, administrative, and healthcare supply chains that are the core to combat the pandemic. Moreover, in this article, we highlight various present and future application areas for drone technology, which could pave the way for future research and industry applications.     

Understanding the impact of information sources on COVID-19 related preventive measures in Finland

The COVID-19 pandemic amplified the influence of information reporting on human behavior, as people were forced to quickly adapt to a new health threatening situation by relying on new information. Drawing from protection-motivation and cognitive load theories, we formulated a structural model eliciting the impact of the three online information sources: (1) social media, (2) official websites, and (3) other online news sources; on motivation to adopt recommended COVID-19 preventive measures. The model was tested with the data collected from university employees and students (n = 225) in March 2020 through an online survey and analyzed using partial least square structural equation modeling (PLS-SEM). We observed that social media and other online news sources increased information overload amongst the online information sources. This, in turn, negatively affected individuals' self-isolation intention by increasing perceived response costs and decreasing response efficacy. The study highlights the role of online information sources on preventive behaviors during pandemics.     

A new educational normal an intersectionality-led exploration of education,learning technologies,and diversity during COVID-19

The COVID-19 pandemic exacerbated the learning technologies disparity in the U.S. K-12 education system, thus broadening an already existing and troublesome digital divide. Low-income and minority students and families were particularly disadvantaged in accessing hardware and software technologies to support teaching and learning. Moreover, the homicide of George Floyd fostered a new wave of inquiry about racism and inequality, questioning often enabled with and through technology and social media. To address these issues, this article explores how parents and teachers experienced the pandemic through intersectional and digital divide-driven lenses. Data were collected from eight parents of underserved children and nine U.S. K-12 teachers to better understand challenges and best practices related to learning technologies during the pandemic. Data collection also focused on conversations about social justice, exploring specific needs and strategies for addressing technology inclusion and diversity in educational environments. Results from the study suggest that COVID-19 was a source of increased digital divide in terms of community and social support rather than economic means. At the same time, staying at home facilitated family discussions about racism and intersectionality-related themes. Implications are suggested for improving school communities and contexts in dealing with pandemic and emergency learning.