Adverse consequences of emotional support seeking through social network sites in coping with stress from a global pandemic.

This study explores how using social networking sites (SNSs) to cope with stressors induced by a global pandemic (in this case, COVID-19) can have negative consequences. The pandemic has imposed particular stressors on individuals, such as the threats of contracting the virus and of unemployment. Owing to the lockdowns and confinements implemented to limit the spread of the pandemic, SNS use has surged worldwide. Drawing on Lazarus and Folkman's theory of stress and coping, we consider COVID-19 obsession to be an adverse emotional response to the stressors brought about by the pandemic and emotional support seeking through SNS as a coping strategy. Furthermore, we identify SNS exhaustion as an adverse outcome of this form of coping. Finally, we analyze the intention to reduce SNS use as a corrective behavioral outcome to mitigate the negative effect of SNS-mediated coping. The findings indicate that: 1) the threat of the COVID-19 disease and the threat of unemployment drive COVID-19 obsession; 2) COVID-19 obsession contributes to emotional support seeking through SNS; 3) emotional support seeking through SNS exerts a positive effect on SNS exhaustion; 4) SNS exhaustion contributes to the intention to reduce SNS use. Our results advance Information Systems (IS) research by focusing on the use of Information Technology (IT) to cope with stressors that are essentially not IT-related; such research is largely absent from previous literature. Furthermore, our paper contributes to the increasing amount of literature on IT-mediated coping with stressors and reduced social media use.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets.

BACKGROUND: Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users' thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users' insomnia and their Big 5 personality traits as derived from social media interactions. OBJECTIVE: The purpose of this study is to build an insomnia prediction model from users' psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. METHODS: In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users' personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. RESULTS: Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, "no," "not," "never"). Some people frequently use swear words (eg, "damn," "piss," "fuck") with strong temperament. They also use anxious (eg, "worried," "fearful," "nervous") and sad (eg, "crying," "grief," "sad") words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. CONCLUSIONS: Our model can help predict insomnia from users' social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.

A casino in my pocket: Gratifications associated with obsessive and harmonious passion for mobile gambling.

Mobile gambling differs from land-based and traditional forms of gambling in that the opportunity to place bets and engage with casinos is constantly present and easily accessible. Instead of going to a physical bookmaker or casino, mobile gambling is done quickly and swiftly, anytime, anywhere, with a few taps on a mobile device. Previous studies reveal mobile gambling has managed to reach new audiences especially amongst younger people. Gambling harms can have severe adverse effects on individuals, families and society. However, for a subgroup of highly involved individuals, gambling can be considered a harmonious passion that permits frequent gambling without elevating individual's risks of experience problem gambling manifestations. Combining the Uses and Gratifications (U&G) and Dualistic Model of Passion (DMP) frameworks, the present study aims to determine if and how the different gratifications sought from mobile gambling are susceptible to explaining non-problematic versus problematic patterns in highly involved gamblers. Data were collected over two waves from a global sample of mobile gamblers (N = 327). Results emphasize that the motivational underpinnings of mobile gambling (as measured by the U&G) differ in obsessive versus harmonious passion. Obsessive passion is associated with poor mood and problematic gambling. In contrast, harmonious passion for mobile gambling is associated with positive mood but is unrelated to problematic gambling. Based on these findings, and given that problematic gambling is an internationally relevant public health issue (the prevalence of problem gambling is estimated to range from 0.1% to 5.8% in different countries), we suggest interventions focusing on specific uses and gratifications associated with an obsessive passion for mobile gambling may be effective in reducing problematic usage patterns.

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.

Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective.

Social media plays a significant role during pandemics such as COVID-19, as it enables people to share news as well as personal experiences and viewpoints with one another in real-time, globally. Building off the affordance lens and cognitive load theory, we investigate how motivational factors and personal attributes influence social media fatigue and the sharing of unverified information during the COVID-19 pandemic. Accordingly, we develop a model which we analyse using the structural equation modelling and neural network techniques with data collected from young adults in Bangladesh (N = 433). The results show that people, who are driven by self-promotion and entertainment, and those suffering from deficient self-regulation, are more likely to share unverified information. Exploration and religiosity correlated negatively with the sharing of unverified information. However, exploration also increased social media fatigue. Our findings indicate that the different use purposes of social media introduce problematic consequences, in particular, increased misinformation sharing.

Impact of Online Information on Self-Isolation Intention During the COVID-19 Pandemic: Cross-Sectional Study.

BACKGROUND: During the coronavirus disease (COVID-19) pandemic, governments issued movement restrictions and placed areas into quarantine to combat the spread of the disease. In addition, individuals were encouraged to adopt personal health measures such as social isolation. Information regarding the disease and recommended avoidance measures were distributed through a variety of channels including social media, news websites, and emails. Previous research suggests that the vast amount of available information can be confusing, potentially resulting in overconcern and information overload. OBJECTIVE: This study investigates the impact of online information on the individual-level intention to voluntarily self-isolate during the pandemic. Using the protection-motivation theory as a framework, we propose a model outlining the effects of cyberchondria and information overload on individuals' perceptions and motivations. METHODS: To test the proposed model, we collected data with an online survey (N=225) and analyzed it using partial least square-structural equation modeling. The effects of social media and living situation were tested through multigroup analysis. RESULTS: Cyberchondria and information overload had a significant impact on individuals' threat and coping perceptions, and through them on self-isolation intention. Among the appraisal constructs, perceived severity (P=.002) and self-efficacy (P=.003) positively impacted self-isolation intention, while response cost (P<.001) affected the intention negatively. Cyberchondria (P=.003) and information overload (P=.003) indirectly affected self-isolation intention through the aforementioned perceptions. Using social media as an information source increased both cyberchondria and information overload. No differences in perceptions were found between people living alone and those living with their families. CONCLUSIONS: During COVID-19, frequent use of social media contributed to information overload and overconcern among individuals. To boost individuals' motivation to adopt preventive measures such as self-isolation, actions should focus on lowering individuals' perceived response costs in addition to informing them about the severity of the situation.

Investigating usability of mobile health applications in Bangladesh.

BACKGROUND: Lack of usability can be a major barrier for the rapid adoption of mobile services. Therefore, the purpose of this paper is to investigate the usability of Mobile Health applications in Bangladesh. METHOD: We followed a 3-stage approach in our research. First, we conducted a keyword-based application search in the popular app stores. We followed the affinity diagram approach and clustered the found applications into nine groups. Second, we randomly selected four apps from each group (36 apps in total) and conducted a heuristic evaluation. Finally, we selected the highest downloaded app from each group and conducted user studies with 30 participants. RESULTS: We found 61% usability problems are catastrophe or major in nature from heuristic inspection. The most (21%) violated heuristic is aesthetic and minimalist design. The user studies revealed low System Usability Scale (SUS) scores for those apps that had a high number of usability problems based on the heuristic evaluation. Thus, the results of heuristic evaluation and user studies complement each other. CONCLUSION: Overall, the findings suggest that the usability of the mobile health apps in Bangladesh is not satisfactory in general and could be a potential barrier for wider adoption of mobile health services.

A Systematic Review of the Digital Interventions for Fighting COVID-19: The Bangladesh Perspective.

The objective of this paper is to synthesize the digital interventions initiatives to fight against COVID-19 in Bangladesh and compare with other countries. In order to obtain our research objective, we conducted a systematic review of the online content. We first reviewed the digital interventions that have been used to fight against COVID-19 across the globe. We then reviewed the initiatives that have been taken place in Bangladesh. Thereafter, we present a comparative analysis between the initiatives taken in Bangladesh and the other countries. Our findings show that while Bangladesh is capable to take benefits of the digital intervention approaches, tighter cooperation between government and private organizations as well as universities would be needed to get the most benefits. Furthermore, the government needs to make sure that the privacy of its citizens are protected.

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper. Impact Statement: Artificial intelligence (AI) and machine learning(ML) methods have been widely used to assist in the fight against COVID-19 pandemic. A very few in-depth literature reviews have been conducted to synthesize the knowledge and identify future research agenda including a previously published review on data science for COVID-19 in this article. In this article, we synthesized reviewed recent literature that focuses on the usages and applications of AI and ML to fight against COVID-19. We have identified seven future research directions that would guide researchers to conduct future research. The most significant of these are: develop new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect and variation in research outcomes based on different types of data.

A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis.

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted. Thirdly, the extracted data were analyzed through the affinity diagram, noticing-collecting-thinking, and descriptive analysis. As outcomes, the review provides a state-of-the-art view of mobile apps developed for COVID-19 by revealing nine functionalities or features. It revealed ten factors related to information systems design characteristics that can guide future app design. The review outcome highlights the need for new development and further refinement of the existing applications considering not only the revealed objectives and their associated functionalities, but also revealed design characteristics such as reliability, performance, usefulness, supportive, security, privacy, flexibility, responsiveness, ease of use, and cultural sensitivity.

Did location-based games motivate players to socialize during COVID-19?

Location-based games (LBGs) are typically played outdoors, as moving in the game is done by moving in the real world. However, during the COVID-19 pandemic, people were advised and even forced by governments to stay home and avoid social contact to slow down the spreading of the virus. The major LBG developers reacted by making in-game adjustments that allow playing from home, while still maintaining some incentives for players to go outdoors and socialise. For investigating factors influencing intention to play LBGs socially during the on-going pandemic, we collected cross-sectional survey data (N = 855) from Finnish players of the most popular LBG, Pokémon GO. The results showed that perceived severity of the pandemic and a positive attitude towards both governmental measures and in-game changes for combatting COVID-19 predicted intention to reduce social playing. Fear of missing out and deficient self-regulation increased playing intensity, which in turn negatively correlated with the intention to reduce social playing. Our findings demonstrate the influence that LBGs can have on human behaviour even during global crises such as COVID-19. As such, LBGs can be considered a resource in designing interventions for influencing movement at a population level.