Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

Promote citizen engagement with warnings - an empirical examination of government social media accounts during public health crises

BACKGROUND

Effective warnings are important for preventing the spread of disease during the early stages of outbreaks. Social media serves as a valuable platform for disseminating warning messages. The success of warnings issued through government social media accounts (GSMAs) depends on citizen engagement. However, an incomplete understanding of the relationship between warning messages and audience responses has hindered the design of crisis communication strategies.

METHODS

We investigated the factors affecting citizen engagement with warnings on GSMAs during public health crises. Drawing on the Elaboration Likelihood Model (ELM) and the Crisis and Emergency Risk Communication (CERC) framework, model was developed to analyze the effects of central routes (content features) and peripheral routes (microstructural and source features) on citizen engagement, as well as the moderating effect of disease type. Data were collected from 38 Sina Weibo accounts of government agencies in China during two public health crises: COVID-19 and H1N1. Logit regression analysis was conducted to test the hypothesized relationships.

RESULTS

The results indicate that (1) positive emotional tendencies and more warning elements are associated with citizen engagement; (2) the relationship between message length and citizen engagement follows an inverted U-shape; (3) media richness and information style variety significantly enhance citizen engagement; and (4) disease type (emerging vs. reemerging infectious diseases) moderates the relationships between media richness, information style variety, source influence, and citizen engagement.

CONCLUSIONS

Given that issuing warnings is critical to emergency management, our findings provide significant theoretical and practical insights, particularly for improving early government-public communication through social media platforms.

TRIAL REGISTRATION

Not applicable.

Effects of information involvement on subjective well-being during public health emergencies: the mediating roles of emotional regulation and social support

Subjective well-being is an important criterion to measure the quality of individual life. Based on social support theory and emotional regulation theory, this research tests the effects of individual and environmental factors on subjective well-being during public health emergencies. 1,488 valid samples were collected through an online questionnaire survey. The results show that: (1) Individuals' perceived involvement of information related to public health emergency significantly influences their generalized anxiety and social media self-disclosure; (2) Generalized anxiety during public health emergency affects subjective well-being through emotional regulation and social expectation; (3) Social media self-disclosure during a public health emergency affects subjective well-being through social support and social expectation; (4) Social loneliness negatively moderates the effect of emotional regulation on subjective well-being, with lower loneliness strengthening this effect; (5) Social anxiety positively moderates the effect of social support on subjective well-being, with higher anxiety strengthening this effect. This study provides insights for the public to better cope with public health emergencies and improve their subjective well-being through adjusting their emotion and seeking social support.

When Infodemic Meets Epidemic: Systematic Literature Review

BACKGROUND

Epidemics and outbreaks present arduous challenges, requiring both individual and communal efforts. The significant medical, emotional, and financial burden associated with epidemics creates feelings of distrust, fear, and loss of control, making vulnerable populations prone to exploitation and manipulation through misinformation, rumors, and conspiracies. The use of social media sites has increased in the last decade. As a result, significant amounts of public data can be leveraged for biosurveillance. Social media sites can also provide a platform to quickly and efficiently reach a sizable percentage of the population; therefore, they have a potential role in various aspects of epidemic mitigation.

OBJECTIVE

This systematic literature review aimed to provide a methodical overview of the integration of social media in 3 epidemic-related contexts: epidemic monitoring, misinformation detection, and the relationship with mental health. The aim is to understand how social media has been used efficiently in these contexts, and which gaps need further research efforts.

METHODS

Three research questions, related to epidemic monitoring, misinformation, and mental health, were conceptualized for this review. In the first PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) stage, 13,522 publications were collected from several digital libraries (PubMed, IEEE Xplore, ScienceDirect, SpringerLink, MDPI, ACM, and ACL) and gray literature sources (arXiv and ProQuest), spanning from 2010 to 2022. A total of 242 (1.79%) papers were selected for inclusion and were synthesized to identify themes, methods, epidemics studied, and social media sites used.

RESULTS

Five main themes were identified in the literature, as follows: epidemic forecasting and surveillance, public opinion understanding, fake news identification and characterization, mental health assessment, and association of social media use with psychological outcomes. Social media data were found to be an efficient tool to gauge public response, monitor discourse, identify misleading and fake news, and estimate the mental health toll of epidemics. Findings uncovered a need for more robust applications of lessons learned from epidemic "postmortem documentation." A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies.

CONCLUSIONS

Harnessing the full potential of social media in epidemic-related tasks requires streamlining the results of epidemic forecasting, public opinion understanding, and misinformation detection, all while keeping abreast of potential mental health implications. Proactive prevention has thus become vital for epidemic curtailment and containment.

Linguistic indicators for predicting the veracity of online health rumors

This study aims to examine the role of language in discerning the authenticity of online health rumors. To achieve this goal, it specifically focuses on analyzing five categories of linguistic indicators: (1) emotional language characterized by sentiment words, sensory words, and continuous punctuations, (2) exaggerated language defined by the presence of extreme numbers and extreme adverbs, (3) personalized language denoted by first-person pronouns, (4) unprofessional language represented by typographical errors, and (5) linkage language marked by inclusion of hyperlinks. To conduct the investigation, a dataset consisting of 1,500 information items was utilized. The dataset exhibited a distribution pattern wherein 20% of the information was verified to be true, while the remaining 80% was categorized as rumors. These items were sourced from two prominent rumor-clarification websites in China. A binomial logistic regression was used for data analysis to determine whether the language used in an online health rumor could predict its authenticity. The results of the analysis showed that the presence of sentiment words, continuous punctuation marks, extreme numbers and adverbs in an online health rumor could predict its authenticity. Personalized language, typographical errors, and hyperlinks were also found to be useful indicators for identifying health rumors using linguistic indicators. These results provide valuable insights for identifying health rumors using language-based features and could help individuals and organizations better understand the credibility of online health information.

Identifying drivers of COVID-19 vaccine sentiments for effective vaccination policy

The COVID-19 pandemic has had far-reaching consequences globally, including a significant loss of lives, escalating unemployment rates, economic instability, deteriorating mental well-being, social conflicts, and even political discord. Vaccination, recognized as a pivotal measure in mitigating the adverse effects of COVID-19, has evoked a diverse range of sentiments worldwide. In particular, numerous users on social media platforms have expressed concerns regarding vaccine availability and potential side effects. Therefore, it is imperative for governmental authorities and senior health policy strategists to gain insights into the public's perspectives on vaccine mandates in order to effectively implement their vaccination initiatives. Despite the critical importance of comprehending the underlying factors influencing COVID-19 vaccine sentiment, the existing literature offers limited research studies on this subject matter. This paper presents an innovative methodology that harnesses Twitter data to extract sentiment pertaining to COVID-19 vaccination through the utilization of Artificial Intelligence techniques such as sentiment analysis, entity detection, linear regression, and logistic regression. The proposed methodology was applied and tested on live Twitter feeds containing COVID-19 vaccine-related tweets, spanning from February 14, 2021, to April 2, 2023. Notably, this approach successfully processed tweets in 45 languages originating from over 100 countries, enabling users to select from an extensive scenario space of approximately 3.55 × 10249 possible scenarios. By selecting specific scenarios, the proposed methodology effectively identified numerous determinants contributing to vaccine sentiment across iOS, Android, and Windows platforms. In comparison to previous studies documented in the existing literature, the presented solution emerges as the most robust in detecting the fundamental drivers of vaccine sentiment and demonstrates the vaccination sentiments over a substantially longer period exceeding 24 months.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

"Do as I say but not as I do": Influence of political leaders' populist communication styles on public adherence in a crisis using the global case of COVID-19 movement restrictions

This paper explores the influence of political leaders' populist communication styles on public adherence to government policies regarding COVID-19 containment. We adopt a mixed-methods approach that combines: theory building with a nested multicase study design for Study 1 and an empirical study in a natural setting for Study 2. Based on the results from Studies 1 and 2, we develop two propositions that we further explain theoretically: (P1) countries with political leaders associated with engaging or intimate populist communication styles (i.e., the UK, Canada, Australia, Singapore, and Ireland) exhibit better public adherence to their governments' COVID-19 movement restrictions than do countries with political leaders associated with communication styles that combine the champion of the people and engaging styles (i.e., the US); (P2) the country whose political leader is associated with a combination of engaging and intimate populist communication styles (i.e., Singapore) exhibits better public adherence to the government's COVID-19 movement restrictions than do countries whose political leaders adopted solely engaging or solely intimate styles, namely, the UK, Canada, Australia, and Ireland. This paper contributes to the research on political leadership in crises and populist political communication.

Protecting public's wellbeing against COVID-19 infodemic: The role of trust in information sources and rapid dissemination and transparency of information over time

Objectives

This study examined how trust in the information about COVID-19 from social media and official media as well as how the information was disseminated affect public's wellbeing directly and indirectly through perceived safety over time.

Methods

Two online surveys were conducted in China, with the first survey (Time1, N = 22,718) being at the early stage of the pandemic outbreak and the second one (Time 2, N = 2,901) two and a half years later during the zero-COVID policy lockdown period. Key measured variables include trust in official media and social media, perceived rapid dissemination and transparency of COVID-19-related information, perceived safety, and emotional responses toward the pandemic. Data analysis includes descriptive statistical analysis, independent samples t-test, Pearson correlations, and structural equation modeling.

Results

Trust in official media, perceived rapid dissemination and transparency of COVID-19-related information, perceived safety, as well as positive emotional response toward COVID-19 increased over time, while trust in social media and depressive response decreased over time. Trust in social media and official media played different roles in affecting public's wellbeing over time. Trust in social media was positively associated with depressive emotions and negatively associated with positive emotion directly and indirectly through decreased perceived safety at Time 1. However, the negative effect of trust in social media on public's wellbeing was largely decreased at Time 2. In contrast, trust in official media was linked to reduced depressive response and increased positive response directly and indirectly through perceived safety at both times. Rapid dissemination and transparency of COVID-19 information contributed to enhanced trust in official media at both times.

Conclusion

The findings highlight the important role of fostering public trust in official media through rapid dissemination and transparency of information in mitigating the negative impact of COVID-19 infodemic on public's wellbeing over time.

The effects of social-media based social comparison information and similarity mindsets on COVID-19 vaccination uptake cognitions

Vaccine hesitancy-delays in vaccine uptake when one is readily available-is an important public health issue. During the COVID-19 pandemic, the role of psychosocial factors in influencing cognitions and behaviors related to vaccine uptake have been examined. Using an online sample of unvaccinated U.S. adults (N = 300), we examined the influence of COVID-19-related social media-based comparison information (e.g., others' attitudes about taking the vaccine)-as well as the moderating impact of (dis)similarity mindsets and indirect influence of affective associations, norm perceptions, and self-evaluations of efficacy-on vaccination uptake intentions. Participants reported higher intentions for vaccine uptake following exposure to cautious comparison models (e.g., those that engaged in health prevention behaviors, intended to get vaccinated) versus risky comparison models (e.g., those who did not engage in health prevention behaviors, did not intend to get vaccinated) and neutral comparison models and this effect was indirect through positive affective associations about taking the vaccine. There were no main or interactive effects of (dis)similarity mindsets. Understanding the psychosocial factors that influence health cognitions and behaviors in the context of an infectious disease pandemic will advance theoretical development and aid in creating interventions targeting vaccine uptake.

Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Reddit language indicates changes associated with diet, physical activity, substance use, and smoking during COVID-19

COVID-19 has adversely impacted the health behaviors of billions of people across the globe, modifying their former trends in health and lifestyle. In this paper, we compare the psychosocial language markers associated with diet, physical activity, substance use, and smoking before and after the onset of COVID-19 pandemic. We leverage the popular social media platform Reddit to analyze 1 million posts between January 6, 2019, to January 5, 2021, from 22 different communities (i.e., subreddits) that belong to four broader groups-diet, physical activity, substance use, and smoking. We identified that before the COVID-19 pandemic, posts involved sharing information about vacation, international travel, work, family, consumption of illicit substances, vaping, and alcohol, whereas during the pandemic, posts contained emotional content associated with quarantine, withdrawal symptoms, anxiety, attempts to quit smoking, cravings, weight loss, and physical fitness. Prevalent topic analysis showed that the pandemic was associated with discussions about nutrition, physical fitness, and outdoor activities such as backpacking and biking, suggesting users' focus shifted toward their physical health during the pandemic. Starting from the week of March 23, 2020, when several stay-at-home policies were enacted, users wrote more about coping with stress and anxiety, alcohol misuse and abuse, and harm-reduction strategies like switching from hard liquor to beer/wine after people were socially isolated. In addition, posts related to use of substances such as benzodiazepines (valium, xanax, clonazepam), nootropics (kratom, phenibut), and opioids peaked around March 23, 2020, followed by a decline. Of note, unlike the general decline observed, the volume of posts related to alternatives to heroin (e.g., fentanyl) increased during the COVID-19 pandemic. Posts about quitting smoking gained momentum after late March 2020, and there was a sharp decline in posts about craving to smoke. This study highlights the significance of studying social media discussions on platforms like Reddit which are a rich ecological source of human experiences and provide insights to inform targeted messaging and mitigation strategies, and further complement ongoing traditional primary data collection methods.

Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification

User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the "July 20 heavy rainstorm in Zhengzhou" posted on China's largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users' attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.

Media consumption and mental health during COVID-19 lockdown: a UK cross-sectional study across England, Wales, Scotland and Northern Ireland

Aim

As individuals adjust to new 'norms' and ways of living during the COVID-19 lockdown, there is a continuing need for up-to-date information and guidance. Evidence suggests that frequent media exposure is related to a higher prevalence of mental health problems, especially anxiety and depression. The aim of this study was to determine whether COVID-19 related media consumption is associated with changes in mental health outcomes.

Methods

This paper presents baseline data from the COVID-19 Psychological Wellbeing Study. The cross-sectional study data was collected using an online survey following the Generalised Anxiety Disorder scale (GAD-7) and the Patient Health Questionnaire (PHQ-9), with some other basic information collected. Logistic regression analysis was used to examine the influence of socio-demographic and media specific factors on anxiety and depression.

Results

The study suggested that media usage is statistically significantly associated with anxiety and depression on the GAD-7 and PHQ-9 scales with excessive media exposure related to higher anxiety and depression scores.

Conclusion

This study indicated that higher media consumption was associated with higher levels of anxiety and depression. Worldwide it should be acknowledged that excessive media consumption, particularly social media relating to COVID-19, can have an effect on mental health. However, as this was a cross-sectional study we cannot infer any directionality as we cannot infer cause and effect; therefore, future research involving longitudinal data collection and analyses of variables over time is warranted.

Does the Power of Social Example Fade? Nudge Effect of Social Information on Individual's Donation Behaviors During the COVID-19 Pandemic: A Moderated Mediation Model with Three-Wave Cross-Sectional Data

Purpose

This study assesses how various social information influence individuals' money donation behaviors towards charitable funds against the COVID-19 pandemic at different stages of the pandemic. It also explores the mediating role of social anxiety and the moderating role of self-control.

Materials and Methods

This three-wave study was conducted with online survey experiments using convenience sampling at the pandemic's outbreak stage (April-June 2020), trough stage (February-March 2021), and resurgence stage (May 2022) in China. The nudge power of social information was measured by whether participants changed their initial money donation decisions after informed positive or negative social information. Self-report scales were used to measure levels of social anxiety (Social Interaction Anxiety Scale) and self-control (Self-Control Scale). The final data set included 1371 participants from 26 provinces of mainland China. Stata medeff package and SPSS PROCESS were used to analyze the data.

Results

Individuals' initial donation behaviors did not fluctuate along with the pandemic status, but the nudge effect of social information did. From outbreak stage to trough stage, the nudge power of positive social information significantly declined, but did not significantly change again at the resurgence stage. By contrast, the nudge power of negative social information did not significantly differ between outbreak and trough stage but did significantly increase at the resurgence stage. Social anxiety played a significant mediating role in the relationship between COVID-19 status and power of social information. Moreover, self-control moderated the direct effect of COVID-19 status on power of social information and the indirect effect via social anxiety.

Conclusion

Our findings enrich research on the nudge power variation of social information on individuals' donation behaviors along with the pandemic status and its potential psychological influence factors. This study also helps guide organizations to better design and carry out social information nudge mechanism.

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19

COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is fine-tuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets.

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations

Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for instance, is crucial for the success of the global vaccination programs and thus achieving herd immunity. To combat the proliferation of COVID-19-related misinformation, considerable research efforts have been and are still being dedicated to building and sharing COVID-19 misinformation detection datasets and models for Arabic and other languages. However, most of these datasets provide binary (true/false) misinformation classifications. Besides, the few studies that support multi-class misinformation classification deal with a small set of misinformation classes or mix them with situational information classes. False news stories about COVID-19 are not equal; some tend to have more sinister effects than others (e.g., fake cures and false vaccine info). This suggests that identifying the sub-type of misinformation is critical for choosing the suitable action based on their level of seriousness, ranging from assigning warning labels to the susceptible post to removing the misleading post instantly. We develop comprehensive annotation guidelines in this work that define 19 fine-grained misinformation classes. Then, we release the first Arabic COVID-19-related misinformation dataset comprising about 6.7K tweets with multi-class and multi-label misinformation annotations. In addition, we release a version of the dataset to be the first Twitter Arabic dataset annotated exclusively with six different situational information classes. Identifying situational information (e.g., caution, help-seeking) helps authorities or individuals understand the situation during emergencies. To confirm the validity of the collected data, we define three classification tasks and experiment with various machine learning and transformer-based classifiers to offer baseline results for future research. The experimental results indicate the quality and validity of the data and its suitability for constructing misinformation and situational information classification models. The results also demonstrate the superiority of AraBERT-COV19, a transformer-based model pretrained on COVID-19-related tweets, with micro-averaged F-scores of 81.6% and 78.8% for the multi-class misinformation and situational information classification tasks, respectively. Label Powerset with linear SVC achieved the best performance among the presented methods for multi-label misinformation classification with micro-averaged F-scores of 76.69%.

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Cyber violence caused by the disclosure of route information during the COVID-19 pandemic

Disclosure of patients' travel route information by government departments has been an effective and indispensable pandemic prevention and control measure during the COVID-19 pandemic. However, this measure may make patients susceptible to cyber violence (CV). We selected 13 real cases that occurred in China during the COVID-19 pandemic for analysis. We identified several characteristics that commonly appeared due to route information, such as rumors about and moral condemnation of patients, and determined that patients who are the first locally confirmed cases of a particular wave of the pandemic are more likely to be the victims of CV. We then analyzed and compared six real cases using data mining and network analysis approaches. We found that disclosing travel route information increases the risk of exposing patients to CV, especially those who violate infection prevention regulations. In terms of disseminating information, we found that mainstream media and influential we-media play an essential role. Based on the findings, we summarized the formation mechanism of route information disclosure-caused CV and proposed three practical suggestions-namely, promote the publicity of the media field with the help of mainstream media and influential we-media, optimize the route information collection and disclosure system, and ease public anxiety about the COVID-19 pandemic. To our knowledge, this study is one of the first to focus on CV on social media during the COVID-19 pandemic. We believe that our findings can help governments better carry out pandemic prevention and control measures on a global scale.

Young informal carers’ information needs communicated online: Professional and personal growth, finance, health and relationships

Young informal carers (YICs) are non-professional young individuals providing care and support in various forms, usually to immediate family members, afflicted from a diverse range of both long- and short-term health conditions. Although there is significant knowledge about the information needs of adult carers in general, information needs and information seeking characteristics of the YICs’ community are understudied and are different. This study aims to identify the information needs of YICs communicated over the Internet and understanding their information seeking characteristics through a three-stage qualitative content analysis of posts written by YICs on two notable Internet forums. The analysis of 323 posts dated between March 2010 and April 2019 finds YICs’ needs are categorised by two types of online expression of needs, situational and information. Situational needs are illustrations of current difficult conditions and information needs are direct requests for information. Under situational and information needs, we identify four types of needs expressed: personal and professional growth, health (self and caree), finance and relationships. In addition, the findings indicate 94.36% posts in the sample as situational needs, which depict the uncertainty experienced by YICs under caring circumstances. The findings can assist government organisations and charities by improving the indexing of advice pages of their websites appropriate to the YICs’ search words, better availability of information and advertising, in addition to building quality mobile applications or digital support tools.

Characteristics of Collective Resilience and Its Influencing Factors from the Perspective of Psychological Emotion: A Case Study of COVID-19 in China

Collective resilience is the ability of human beings to adapt and collectively cope with crises in adversity. Emotional expression is the core element with which to characterize the psychological dimension of collective resilience. This research proposed a stage model of collective resilience based on the temporal evolution of the public opinions of COVID-19 in China's first anti-pandemic cycle; using data from hot searches and commentaries on Sina Weibo, the changes in the emotional patterns of social groups are revealed through analyses of the sentiments expressed in texts. A grounded theory approach is used to elucidate the factors influencing collective resilience. The research results show that collective resilience during the pandemic exhibited an evolutionary process that could be termed, "preparation-process-recovery". Analyses of expressed sentiments reveal an evolutionary pattern of "positive emotion prevailing-negative emotion appearing-positive emotion recovering Collective resilience from a psycho-emotional perspective is the result of "basic cognition-intermediary condition-consequence" positive feedback, in which the basic cognition is expressed as will embeddedness and the intermediary conditions include the subject behavior and any associated derived behavioral characteristics and spiritual connotation. These results are significant both theoretically and practically with regard to the reconstruction of collective resilience when s' force majeure' event occur.

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.

Understanding social media users' engagement intention toward emergency information: the role of experience and information usefulness in a reciprocity framework

Purpose

The aim of this study is to investigate how social media users' experience of seeking emergency information affects their engagement intention toward emergency information with a reciprocity framework integrated with information adoption model.

Design/methodology/approach

Drawing on reciprocity theory, indebtedness theory, and information adoption model, an integrative research model is developed. This study employs a questionnaire survey to collect data of 325 social media users in China. Structural equation modeling analyses are conducted to test the proposed theoretical model.

Findings

Social media users' experience of seeking emergency information has a strong effect on their perceived information usefulness and indebtedness, while perceived information usefulness further influences community norm, indebtedness, and engagement intention. The authors also found that perceived information usefulness mediates the relationships between experience of seeking emergency information and community norm/indebtedness.

Originality/value

This study offers a new perspective to explain social media users' engagement intention in the diffusion of emergency information. This study contributes to the literature by extending the theoretical framework of reciprocity and applying it to the context of emergency information diffusion. The findings of this study could benefit the practitioners who wish to leverage social media tools for emergency response purposes.

Effects of the COVID-19 pandemic on diet and physical activity and the possible influence factors among Saudi in Riyadh

Background/Aim

The COVID-19 pandemic has been spreading throughout the world, having a significant impact on people’s lifestyles and health through social isolation and home confinement. The purpose of this study is to look into the impact of COVID-19 on diet and physical activity, as well as the possible influence factors, among ≥ 13-year-olds in Riyadh, Saudi Arabia.

Materials and methods

In the present study data were collected from 2,649 participants via an online survey. The Google online questionnaire was available from April 23 to May 6, 2020. During the COVID-19 lockdown, the survey asked respondents about their demographic characteristics (gender, age, education, economic income, and occupation), anthropometric data, physical activity, and diet habits.

Results

The study included 2,649 respondents, with 23.3% being male and 76.7% female. The majority of them were in good health and ranged in age from 21 to 29 years. 31% of those polled were overweight, and 14.3% were obese. The majority of respondents have a bachelor’s degree, diploma, or the equivalent, and a monthly family income of ≤ 25.000 SR. Those who were following a healthy diet (32.3%) were unable to maintain it during confinement, with males being affected more than females (42.7%, 29.3%, respectively, P = 0.004), and those most impacted were aged 21–29 years (38.0%, P = 0.046). Furthermore, 59.5% of males significantly failed to continue exercising during confinement compared to females who exercised consistently (P = 0.01). In terms of age, females aged less than 40 increased their exercise rate by about 23.4%, while males aged 40 and up decreased their exercise rate by 25.7% (P = 0.000). Moreover, 40.5% of the subjects’ weight increased, according to the findings. However, there was no significant effect on body mass index, despite the fact that 51% of participants were overweight or obese.

Conclusion

The data showed that the COVID-19 lockdown had a negative impact on maintaining a healthy diet (p = 0.023*) and physical activity (p = 0.000^**).

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India-specific COVID-19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short-term memory, Bi-directional long short-term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

Recognition and Modeling of Crisis Propagation Patterns Combined with Robot Simulation of Social Networks

In order to improve the identification effect of a crisis propagation mode, this paper studies the crisis propagation mode of social networks and uses an intelligent method to identify the crisis propagation mode. Moreover, this paper analyzes and deduces the features of the modulated signal from the two aspects of spectrum and autocorrelation functions and extracts and simulates the features of signals of different modulation types. In addition, this paper verifies the proposed recognition method from two aspects through theoretical experiments and measured data and builds an intelligent model. Through the experimental research, it can be seen that the identification effect and the control effect of the crisis propagation model based on the social network proposed in this paper are relatively obvious.

Role of fake news and misinformation in supply chain disruption: impact of technology competency as moderator

Studies show that COVID-19 has increased the effects of misinformation and fake news that proliferated during the continued crisis and related turbulent environment. Fake news and misinformation can come from various sources such as social media, print media, as well as from electronic media such as instant messaging services and other apps. There is a growing interest among researchers and practitioners on how fake news and misinformation impacts on supply chain disruption. But the limited research in this area leaves a gap. With this background, the purpose of this study is to determine the role of fake news and misinformation in supply chain disruption and the consequences to a firm's operational performance. This study also investigates the moderating role of technology competency in supply chain disruption and operational performance of the firm. With the help of theories and literature, a theoretical model has been developed. Later, the conceptual model has been validated using partial least squares structural equation modeling. The study finds that there is a significant impact of misinformation and fake news on supply chain disruption, which in turn negatively impacts firms' operational performance. The study also highlights that firms' technology competency can improve the supply chain situation that has been disrupted by misinformation and fake news.

Automating Global Threat-Maps Generation via Advancements of News Sensors and AI

Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countries, and all possible major social media sites. The study revealed 11,668 troubled locations classified successfully with outstanding precision, recall, and F1-score, all evaluated in ubiquitous environment covering mobile, tablet, desktop, and cloud platforms. The system generated interesting global threat maps for robust scenario set of $$3.71 \times {10}^{29}$$ 3.71 × 10 29 , to be reported as original fully autonomous remote sensing application of this kind. The research discloses attractive news and global threat-maps with trusted overall classification accuracy.

Network Sentiment Analysis of College Students in Different Epidemic Stages Based on Text Clustering

In order to analyze the evolution trend of public opinion in emergencies and explore its evolution law, this paper constructs a network sentiment analysis model based on text clustering, where the emotion analysis part is based on the pretraining BERT model and BiGRU model, in which BERT is used as the word embedding model to extract the feature vector of emotional text and BiGRU is used to extract the context of the text feature vector to accurately identify the sentiment polarity of public opinion data. In addition, the K-means clustering algorithm and Kolmogorov-Smirnov Z test were used to divide the different epidemic stages. Compared with other methods, the model proposed in this paper has a great degree of improvement in accuracy, recall, and F1 score index, which provides an opportunity reference and effective detection means for schools at all levels to carry out timely mental health education and psychological intervention for students.

The experimental research on leaders and cooperative behavior

Leaders are critical to a team or organization, their behavior affects employees' psychology and their work effort, and then affects the efficiency and innovation of the team or organization. Previous studies have focused on the role model of leaders, ignoring the guiding role of leaders with different efforts. This paper introduces leader decision-making into the game of public goods to investigate the exemplary role of leaders in behavior decision-making. It divides them into three types by setting the investment amount of leaders to explore the mechanism of leaders' influence in behavior decision-making and behavior change of team members when facing the transformation of leaders with different investment types. This research can provide a significant reference value for enterprises and social organizations on how to play the role of leaders.

The Complex Media Effects on Civic Participation Intention Amid COVID-19 Pandemic: Empirical Evidence from Wuhan College Students

In the context of the COVID-19 pandemic, media exposure is crucial to motivate public action for the combat with COVID-19 pandemic. However, media effects on civic participation intention are understudied. This study applied the Differential Susceptibility to Media effects Model (DSMM) to explore the relations among Wuhan college students' media use, their pandemic-relevant beliefs, and civic participation intention, with a focus on the possible mediation of pandemic-relevant beliefs. Data of 4355 students from a large-scale cross-sectional survey were analyzed. Results show that traditional media use and online media interaction both directly and indirectly affect civic participation intention via pandemic-relevant beliefs. Pandemic-relevant beliefs distort the relations that direct and indirect effects of new media use on civic participation intention are significant but in opposite directions. The influence of pandemic news on civic participation intention is entirely mediated by pandemic-relevant beliefs. To conclude, during pandemic, the role of traditional media use is unreplaceable in its direct and indirect impact on civic participation intention. Pandemic-relevant beliefs play as a distorter variable. The balance between overexposure and insufficiency of pandemic-relevant news is vital. Online media interaction, as a main trait of new media use, plays a crucial role in civic participation intention, directly and indirectly.

Can people hear others' crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China

Social media like Weibo has become an important platform for people to ask for help during COVID-19 pandemic. Using a complete dataset of help-seeking posts on Weibo during the COVID-19 outbreak in China (N = 3,705,188), this study mapped their characteristics and analyzed their relationship with the epidemic development at the aggregate level, and examined the influential factors to determine whether and the extent the help-seeking crying could be heard at the individual level using computational methods for the first time. It finds that the number of help-seeking posts on Weibo has a Granger causality relationship with the number of confirmed COVID-19 cases with a time lag of eight days. This study then proposes a 3C framework to examine the direct influence of content, context, and connection on the responses (measured by retweets and comments) and assistance that help-seekers might receive as well as their indirect effects on assistance through the mediation of both retweets and comments. The differential influences of content (theme and negative sentiment), context (Super topic community, spatial location of posting, and the period of sending time), and connection (the number of followers, whether mentioning others, and verified status of authors and sharers) have been reported and discussed.

Enhancing Warning, Situational Awareness, Assessment and Education in Managing Emergency: Case Study of COVID-19

The volume of network and Internet traffic is increasing extraordinarily fast daily, creating huge data. With this volume, variety, speed, and precision of data, it is hard to collect crisis information in such a massive data environment. This paper proposes a hybrid of deep convolutional neural network (CNN)-long short-term memory (LSTM)-based model to efficiently retrieve crisis information. Deep CNN is used to extract significant characteristics from multiple sources. LSTM is used to maintain long-term dependencies in extracted characteristics while preventing overfitting on recurring connections. This method has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach allows integrating artificial intelligence technologies, deep learning and social media in managing crisis model. It is based on an extension of our previous approach namely long short-term memory-based disaster management and education: this experience forms a background for this model. It combines representation training with situational awareness and education, while retrieving template information by combining various search results from multiple sources. We have extended it to improve our managing disaster model and evaluate it in the case of the coronavirus disease 2019 (COVID-19) while achieving promising results.

Visualizing Social Media Research in the Age of COVID-19

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration.

Deriving anti-epidemic policy from public sentiment: A framework based on text analysis with microblog data

Microblog has become the "first scenario" under which the public learn about the epidemic situation and express their opinions. Public sentiment mining based on microblog data can provide a reference for the government's information disclosure, public sentiment guidance and formulation of epidemic prevention and control policy. In this paper, about 200,000 pieces of text data were collected from Jan. 1 to Feb. 26, 2020 from Sina Weibo, which is the most popular microblog website in China. And a public sentiment analysis framework suitable for Chinese-language scenarios was proposed. In this framework, a sentiment dictionary suitable for Chinese-language scenarios was constructed, and Baidu's Sentiment Analysis API was used to calculate the public sentiment indexes. Then, an analysis on the correlation between the public sentiment indexes and the COVID-19 case indicators was made. It was discovered that there is a high correlation between public sentiments and incidence trends, in which negative sentiment is of statistical significance for the prediction of epidemic development. To further explore the source of public negative sentiment, the topics of the public negative sentiment on Weibo was analyzed, and 20 topics in five categories were got. It is found that there is a strong linkage between the hot spots of public concern and the epidemic prevention and control policies. If the policies cover the hot spots of public concern in a timely and effective manner, the public negative sentiment will be effectively alleviated. The analytical framework proposed in this paper also applies to the public sentiment analysis and policy making for other major public events.

Spatiotemporal impacts of human activities and socio-demographics during the COVID-19 outbreak in the US

BACKGROUND

Understanding non-epidemiological factors is essential for the surveillance and prevention of infectious diseases, and the factors are likely to vary spatially and temporally as the disease progresses. However, the impacts of these influencing factors were primarily assumed to be stationary over time and space in the existing literature. The spatiotemporal impacts of mobility-related and social-demographic factors on disease dynamics remain to be explored.

METHODS

Taking daily cases data during the coronavirus disease 2019 (COVID-19) outbreak in the US as a case study, we develop a mobility-augmented geographically and temporally weighted regression (M-GTWR) model to quantify the spatiotemporal impacts of social-demographic factors and human activities on the COVID-19 dynamics. Different from the base GTWR model, the proposed M-GTWR model incorporates a mobility-adjusted distance weight matrix where travel mobility is used in addition to the spatial adjacency to capture the correlations among local observations.

RESULTS

The results reveal that the impacts of social-demographic and human activity variables present significant spatiotemporal heterogeneity. In particular, a 1% increase in population density may lead to 0.63% more daily cases, and a 1% increase in the mean commuting time may result in 0.22% increases in daily cases. Although increased human activities will, in general, intensify the disease outbreak, we report that the effects of grocery and pharmacy-related activities are insignificant in areas with high population density. And activities at the workplace and public transit are found to either increase or decrease the number of cases, depending on particular locations.

CONCLUSIONS

Through a mobility-augmented spatiotemporal modeling approach, we could quantify the time and space varying impacts of non-epidemiological factors on COVID-19 cases. The results suggest that the effects of population density, socio-demographic attributes, and travel-related attributes will differ significantly depending on the time of the pandemic and the underlying location. Moreover, policy restrictions on human contact are not universally effective in preventing the spread of diseases.

A UAV-based portable health clinic system for coronavirus hotspot areas

This study applied the World Health Organization (WHO) guidelines to redesign the Portable Health Clinic (PHC), as a Remote Healthcare System (RHS), for the spread of COVID-19 containment. Additionally, the proposed drone-based system not only collects people data but also classifies the case according to the main symptoms of coronavirus using the COVID-19 triage process (CT-process) based on the analysis of measurement readings taken from patients, where drones are used in a swarm as a PHC platform and are equipped with the required sensors and essential COVID-19 medications for testing and treating people at their doorstep autonomously when a full curfew is imposed. This paper describes a complete framework and proposes currently in production hardware to build the suggested system, considering the effect of the extra payload weight on drone's durability. In addition, part of the proposed application was simulated using OPNET simulation tool. This work highlights the main aspects that should be considered when designing drone swarm-based system and distributing the roles on system nodes with the main focus on the controlling messages for inter-swarm and intra-swarm communication and coordination.

Sentiment analysis of COVID-19 social media data through machine learning

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries' economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

Three-Phase Methodology to Manage the COVID-19 Information for Classification of Mental Illness

In the COVID-19 era, the use of social media platforms has significantly increased leading to misinformation being produced whose management is quite necessary for the domain experts, such as the Reddit social platform where people disseminate extensive information about their health issues using relevant posts and comments. The management of misinformation about COVID-19 impact on mental illness could be quite beneficial for the domain experts. In this regard, we proposed a two-step methodology which could aid domain experts to manage and group the posts and comments information with respect to COVID-19 impact on mental illness. First, we extract the information of well-known mental illnesses (such as depression, anxiety, OCD and PTSD) from the Raddit platform. Second, we leverage the capabilities of unsupervised learning algorithms and text categorisation approach to manage the information. We also proposed the evaluation model to assess the efficacy of the proposed method according to expert opinion. The experimental results indicate the efficacy of the proposed method. Moreover, we observed fuzzy c-means as an outperformed learner (with [Formula: see text]) as compared to K-means ([Formula: see text]) and Agglomerative ([Formula: see text]).

Hybrid Deep Learning Techniques for Predicting Complex Phenomena: A Review on COVID-19

Complex phenomena have some common characteristics, such as nonlinearity, complexity, and uncertainty. In these phenomena, components typically interact with each other and a part of the system may affect other parts or vice versa. Accordingly, the human brain, the Earth’s global climate, the spreading of viruses, the economic organizations, and some engineering systems such as the transportation systems and power grids can be categorized into these phenomena. Since both analytical approaches and AI methods have some specific characteristics in solving complex problems, a combination of these techniques can lead to new hybrid methods with considerable performance. This is why several types of research have recently been conducted to benefit from these combinations to predict the spreading of COVID-19 and its dynamic behavior. In this review, 80 peer-reviewed articles, book chapters, conference proceedings, and preprints with a focus on employing hybrid methods for forecasting the spreading of COVID-19 published in 2020 have been aggregated and reviewed. These documents have been extracted from Google Scholar and many of them have been indexed on the Web of Science. Since there were many publications on this topic, the most relevant and effective techniques, including statistical models and deep learning (DL) or machine learning (ML) approach, have been surveyed in this research. The main aim of this research is to describe, summarize, and categorize these effective techniques considering their restrictions to be used as trustable references for scientists, researchers, and readers to make an intelligent choice to use the best possible method for their academic needs. Nevertheless, considering the fact that many of these techniques have been used for the first time and need more evaluations, we recommend none of them as an ideal way to be used in their project. Our study has shown that these methods can hold the robustness and reliability of statistical methods and the power of computation of DL ones.

Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic

Novel coronavirus respiratory disease COVID-19 has caused havoc in many countries across the globe. In order to contain infection of this highly contagious disease, most of the world population is constrained to live in a complete or partial lockdown for months together with a minimal human-to-human interaction having far reaching consequences on countries' economy and mental well-being of their citizens. Hence, there is a need for a good predictive model for the health advisory bodies and decision makers for taking calculated proactive measures to contain the pandemic and maintain a healthy economy. This paper extends the mathematical theory of the classical Susceptible-Infected-Removed (SIR) epidemic model and proposes a Generalized SIR (GSIR) model that is an integrative model encompassing multiple waves of daily reported cases. Existing growth function models of epidemic have been shown as the special cases of the GSIR model. Dynamic modeling of the parameters reflect the impact of policy decisions, social awareness, and the availability of medication during the pandemic. GSIR framework can be utilized to find a good fit or predictive model for any pandemic. The study is performed on the COVID-19 data for various countries with detailed results for India, Brazil, United States of America (USA), and World. The peak infection, total expected number of COVID-19 cases and thereof deaths, time-varying reproduction number, and various other parameters are estimated from the available data using the proposed methodology. The proposed GSIR model advances the existing theory and yields promising results for continuous predictive monitoring of COVID-19 pandemic.

Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data

The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from social media data in the early stage of the pandemic, this study explored the correlation between different infectious outcomes of COVID-19 transmission and various factors of the urban environment in the main urban area of Wuhan, utilizing the multiple regression model. The result shows that most spatial factors were strongly correlated to case aggregation areas of COVID-19 in terms of population density, human mobility and environmental quality, which provides urban planners and administrators valuable insights for building healthy and safe cities in an uncertain future.

#WuhanDiary and #WuhanLockdown: gendered posting patterns and behaviours on Weibo during the COVID-19 pandemic

Social media can be both a source of information and misinformation during health emergencies. During the COVID-19 pandemic, social media became a ubiquitous tool for people to communicate and represents a rich source of data researchers can use to analyse users’ experiences, knowledge and sentiments. Research on social media posts during COVID-19 has identified, to date, the perpetuity of traditional gendered norms and experiences. Yet these studies are mostly based on Western social media platforms. Little is known about gendered experiences of lockdown communicated on non-Western social media platforms. Using data from Weibo, China’s leading social media platform, we examine gendered user patterns and sentiment during the first wave of the pandemic between 1 January 2020 and 1 July 2020. We find that Weibo posts by self-identified women and men conformed with some gendered norms identified on other social media platforms during the COVID-19 pandemic (posting patterns and keyword usage) but not all (sentiment). This insight may be important for targeted public health messaging on social media during future health emergencies.

Predicting the number of COVID-19 infections and deaths in USA

BACKGROUND

Uncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants.

RESULTS

Using the COVID-19 initial data reported by Johns Hopkins University, World Health Organization (WHO) and Global Initiative on Sharing All Influenza Data (GISAID), the result shows that the original SAR-COV-2 strain has R0<1 with an initial infection growth rate entropy of 9.11 bits for the United States (U.S.). At close proximity, the average infection time for an infected individual to infect others within a susceptible population is approximately 7 minutes. Assuming no vaccines were available, in the U.S., the number of infections could range between 41,220,199 and 82,440,398 in late March 2022 with approximately, 1,211,036 deaths. However, with the available vaccines, nearly 48 Million COVID-19 cases and 706, 437 deaths have been prevented.

CONCLUSION

The proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.