Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study

Navigating persuasive strategies in online health misinformation: An interview study with older adults on misinformation management

Online health misinformation commonly includes persuasive strategies that can easily deceive lay people. Yet, it is not well understood how individuals respond to misinformation with persuasive strategies at the moment of exposure. This study aims to address the research gap by exploring how and why older adults fall into the persuasive trap of online health misinformation and how they manage their encounters of online health misinformation. Using a think-aloud protocol, semi-structured interviews were conducted with twenty-nine older adults who were exposed to articles employing twelve groups of common persuasive strategies in online health misinformation. Thematic analysis of the transcripts revealed that some participants fell for the persuasive strategies, yet the same strategies were detected by others as cues to pin down misinformation. Based on the participants' own words, informational and individual factors as well as the interplay of these factors were identified as contributors to susceptibility to misinformation. Participants' strategies to manage misinformation for themselves and others were categorized. Implications of the findings are discussed.

Navigating the infodemic: A qualitative study of university students' information strategies during the COVID-19 pandemic

Objectives

We aimed to study the strategies which university students developed for vetting information during the COVID-19 pandemic and associated infodemic.

Methods

We conducted semi-structured interviews with 34 students, using a piloted topic guide which explored several areas of pandemic experiences, including students' use of media. Transcripts were analysed inductively following the thematic approach. Higher order themes were finalised following a coding exercise undertaken by two of the authors.

Results

Participants were acutely aware of misinformation during the pandemic. They rated legacy news media (print and broadcast media with pre-Internet origins) higher than social media for reliable information about the pandemic. However, strikingly, not all legacy media were automatically trusted and not all social media were uniformly distrusted. Participants identified a set of mechanisms for establishing whether a piece of information was truthful and accurate. These mechanisms had four main focal points: (1) the source, (2) the message, (3) individual media literacy and (4) the trustworthiness of others. Despite possessing a critical awareness of misinformation, participants avoided posting anything in relation to the pandemic for fear of becoming the target of online abuse.

Conclusions

In addition to underscoring the role of media literacy, our research foregrounds the need to attend to the importance of fostering media confidence. We define media confidence as the ability of digital media users to challenge and interrogate questionable or inaccurate information safe in the knowledge that there are adequate regulatory mechanisms in place to curb abuse, trolling and intimidation.

Factors associated with Medicare beneficiaries' perceptions of COVID-19 and preventive health behaviors: results from winter 2021 MCBS survey

This study evaluates the impact of preferred information sources on Medicare beneficiaries' perception of COVID-19 severity compared with flu and examines factors influencing preventive health behaviors using the Medicare Current Beneficiary Survey (MCBS) winter 2021. Medicare beneficiaries who primarily relied on traditional news, guidance from government officials, and healthcare providers, beneficiaries who were female, older than 65 years, metro residence, or living in the West were more likely to believe that the COVID-19 is more severe than flu and take vaccine than their counterparts. Compared to White, Black and Hispanic were more likely to agree with COVID-19 severity, but less likely to take vaccine. Factors associated with preventive health behavior utilization included perceived severity of COVID-19, primary information source, gender, race, language, annual income, and chronic health conditions. It is crucial to provide accurate information in lay terms to help people understand the importance of taking preventative actions against COVID-19.  .

Infodemic: Challenges and solutions in topic discovery and data process

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking.

OBJECTIVE

Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022.

METHODS

We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information.

RESULTS

A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions.

CONCLUSIONS

This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

How people interact with a chatbot against disinformation and fake news in COVID-19 in Brazil: The CoronaAI case

BACKGROUND

The search for valid information was one of the main challenges encountered during the COVID-19 pandemic, which resulted in the development of several online alternatives.

OBJECTIVES

To describe the development of a computational solution to interact with users of different levels of digital literacy on topics related to COVID-19 and to map the correlations between user behavior and events and news that occurred throughout the pandemic.

METHOD

CoronaAI, a chatbot based on Google's Dialogflow technology, was developed at a public university in Brazil and made available on WhatsApp. The dataset with users' interactions with the chatbot comprises approximately 7,000 hits recorded throughout eleven months of CoronaAI usage.

RESULTS

CoronaAI was widely accessed by users in search of valuable and updated information on COVID-19, including checking the veracity of possible fake news about the spread of cases, deaths, symptoms, tests and protocols, among others. The mapping of users' behavior revealed that as the number of cases and deaths increased and as COVID-19 became closer, users showed a greater need for information applicable to self-care compared to following the statistical data. In addition, they showed that the constant updating of this technology may contribute to public health by enhancing general information on the pandemic and at the individual level by clarifying specific doubts about COVID-19.

CONCLUSION

Our findings reinforce the potential usefulness of chatbot technology to resolve a wide spectrum of citizens' doubts about COVID-19, acting as a cost-effective tool against the parallel pandemic of misinformation and fake news.

Deepfakes and scientific knowledge dissemination

Science misinformation on topics ranging from climate change to vaccines have significant public policy repercussions. Artificial intelligence-based methods of altering videos and photos (deepfakes) lower the barriers to the mass creation and dissemination of realistic, manipulated digital content. The risk of exposure to deepfakes among education stakeholders has increased as learners and educators rely on videos to obtain and share information. We field the first study to understand the vulnerabilities of education stakeholders to science deepfakes and the characteristics that moderate vulnerability. We ground our study in climate change and survey individuals from five populations spanning students, educators, and the adult public. Our sample is nationally representative of three populations. We found that 27-50% of individuals cannot distinguish authentic videos from deepfakes. All populations exhibit vulnerability to deepfakes which increases with age and trust in information sources but has a mixed relationship with political orientation. Adults and educators exhibit greater vulnerability compared to students, indicating that those providing education are especially susceptible. Vulnerability increases with exposure to potential deepfakes, suggesting that deepfakes become more pernicious without interventions. Our results suggest that focusing on the social context in which deepfakes reside is one promising strategy for combatting deepfakes.

Information Cocoons on Short Video Platforms and Its Influence on Depression Among the Elderly: A Moderated Mediation Model

Background

As the elderly increasingly engage with new media, particularly short video platforms, concerns are arising about the formation of "information cocoons" that limit exposure to diverse perspectives. While the impact of these cocoons on society has been investigated, their effects on the mental well-being of the elderly remain understudied. Given the prevalence of depression among the elderly, it is crucial to understand the potential link between information cocoons and depression among older adults.

Methods

The study examined the relationships between information cocoons and depression, loneliness, and family emotional support among 400 Chinese elderly people. The statistical software package SPSS was used to establish a moderated mediation model between information cocoons and depression.

Results

Information cocoons directly predicted depression among the elderly participants. Family emotional support moderated the first half and the second half of the mediation process, whereby information cocoons affected the depression of the elderly through loneliness. Specifically, in the first half of the mediation process, when the level of information cocoons was lower, the role of family emotional support was more prominent. In the second half of the process, when the level of family emotional support was higher, such support played a more protective role in the impact of loneliness on depression.

Discussion

The findings of this study have practical implications for addressing depression among the elderly population. Understanding the influence of information cocoons on depression can inform interventions aimed at promoting diverse information access and reducing social isolation. These results will contribute to the development of targeted strategies to improve the mental well-being of older adults in the context of evolving media landscapes.

A novel and accurate deep learning-based Covid-19 diagnostic model for heart patients

Using radiographic changes of COVID-19 in the medical images, artificial intelligence techniques such as deep learning are used to extract some graphical features of COVID-19 and present a Covid-19 diagnostic tool. Differently from previous works that focus on using deep learning to analyze CT scans or X-ray images, this paper uses deep learning to scan electro diagram (ECG) images to diagnose Covid-19. Covid-19 patients with heart disease are the most people exposed to violent symptoms of Covid-19 and death. This shows that there is a special, unclear relation (until now) and parameters between covid-19 and heart disease. So, as previous works, using a general diagnostic model to detect covid-19 from all patients, based on the same rules, is not accurate as we prove later in the practical section of our paper because the model faces dispersion in the data during the training process. So, this paper aims to propose a novel model that focuses on diagnosing accurately Covid-19 for heart patients only to increase the accuracy and to reduce the waiting time of a heart patient to perform a covid-19 diagnosis. Also, we handle the only one existed dataset that contains ECGs of Covid-19 patients and produce a new version, with the help of a heart diseases expert, which consists of two classes: ECGs of heart patients with positive Covid-19 and ECGs of heart patients with negative Covid-19 cases. This dataset will help medical experts and data scientists to study the relation between Covid-19 and heart patients. We achieve overall accuracy, sensitivity and specificity 99.1%, 99% and 100%, respectively.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11760-023-02561-8.

Developing personas for live streaming commerce platforms with user survey data

Live streaming commerce has emerged as a novel form of online marketing that offers live streaming commerce platforms a means of meeting different user groups' needs. The objective of this article is to examine the effects of age and gender on live streaming commerce platform usage and investigate user characteristics of these platforms in China. This study adopted a data-driven persona construction method combining quantitative and qualitative methods through the use of survey and interview. The survey involved 506 participants (age range = 19-70), and the interview involved 12 participants. The survey findings showed that age significantly affected users' livestream platform usage, while gender did not. Younger users had higher device proficiency and operation numbers. With more trust and device use, older users used the platforms later in the day than younger users. Interview findings revealed that gender affected users' motivations and value focus. Women tended to use the platforms as a means of entertainment. Women valued service quality and enjoyment more, while men focused on the accuracy of product information more. Four personas with significant differences were then constructed: Dedicated, Dependent, Active and Lurker. Their various needs, motivations and behavior patterns can be considered by designers to elevate the interaction of live streaming commerce platforms.

Seeking innovation: The research protocol for SMEs' networking

The paper aims to state the research protocol for the innovation-seeking behavior of Small- to Medium-sized Enterprises (SMEs), related to the classification of knowledge needs expressed in the networking databases. The dataset of 9301 networking offers as the outcome of proactive attitudes represents the content of the Enterprise Europe Network (EEN) database. The data set has been semi-automatically obtained using the rvest R package, and then analyzed using static word embedding neural network architecture: Continuous Bag-of-Words (CBoW), predictive model Skip-Gram, and Global Vectors for Word Representation (GloVe) considered the state-of-the-art models, to create topic-specific lexicons. The proportion of offers labeled as Exploitative innovation to Explorative innovation is balanced with a 51%-49% proportion. The prediction rates show good performance with an AUC score of 0.887, and the prediction rates for exploratory innovation 0.878 and explorative innovation 0.857. The performance of predictions with the frequency-inverse document frequency (TF-IDF) technique shows that the research protocol is sufficient to categorize the innovation-seeking behavior of SMEs using static word embedding based on the description of knowledge needs and text classification, but it is not perfect due to the general entropy related to the outcome of networking. In the context of networking, SMEs place a greater emphasis on explorative innovation in their innovation-seeking behavior. They prioritize smart technologies and global business cooperation, whereas current information technologies and software are more of interest to SMEs that adopt an exploitative innovation approach.

A scoping review of digital health interventions for combating COVID-19 misinformation and disinformation

OBJECTIVE

We provide a scoping review of Digital Health Interventions (DHIs) that mitigate COVID-19 misinformation and disinformation seeding and spread.

MATERIALS AND METHODS

We applied our search protocol to PubMed, PsychINFO, and Web of Science to screen 1666 articles. The 17 articles included in this paper are experimental and interventional studies that developed and tested public consumer-facing DHIs. We examined these DHIs to understand digital features, incorporation of theory, the role of healthcare professionals, end-user experience, and implementation issues.

RESULTS

The majority of studies (n = 11) used social media in DHIs, but there was a lack of platform-agnostic generalizability. Only half of the studies (n = 9) specified a theory, framework, or model to guide DHIs. Nine studies involve healthcare professionals as design or implementation contributors. Only one DHI was evaluated for user perceptions and acceptance.

DISCUSSION

The translation of advances in online social computing to interventions is sparse. The limited application of behavioral theory and cognitive models of reasoning has resulted in suboptimal targeting of psychosocial variables and individual factors that may drive resistance to misinformation. This affects large-scale implementation and community outreach efforts. DHIs optimized through community-engaged participatory methods that enable understanding of unique needs of vulnerable communities are urgently needed.

CONCLUSIONS

We recommend community engagement and theory-guided engineering of equitable DHIs. It is important to consider the problem of misinformation and disinformation through a multilevel lens that illuminates personal, clinical, cultural, and social pathways to mitigate the negative consequences of misinformation and disinformation on human health and wellness.

Changes in Internet use patterns among older adults in England from before to after the outbreak of the COVID-19 pandemic

The COVID-19 pandemic brought about an increased reliance on the Internet for various daily activities. Given the known digital divide, it is important to understand whether older adults changed their Internet use patterns, but current evidence is limited to cross-sectional studies. This study documents changes in frequency and types of Internet use among older adults from before to shortly after the outbreak of the COVID-19 pandemic (2018/2019 to June/July 2020), and the factors predicting regular use during these early days of the pandemic. Using data on 6,840 adults aged 50 + from the nationally representative English Longitudinal Study of Ageing, we apply longitudinal fixed-effects models to examine within-individual changes in Internet use behaviour. There was no change in the likelihood of daily Internet use between 2018/2019 and June/July 2020, despite the increased digitalisation of services over the pandemic. Daily use in June/July 2020 was negatively related to age, neighbourhood deprivation, and loneliness, and positively related to partnership status, education, employment, income, and organisation membership. Using the Internet for making calls and getting information about Government services increased, which was important given the social restrictions and overall uncertainty. However, Internet use for finding health-related information decreased. As the world moves towards digital alternatives post-pandemic, it is important to continually ensure older adults are not at risk of exclusion.

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.

Social media-based COVID-19 sentiment classification model using Bi-LSTM

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification

BACKGROUND

COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation.

OBJECTIVE

We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA).

METHODS

We collected tweets using CBD- and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives.

RESULTS

We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets.

CONCLUSIONS

This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.

Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches

BACKGROUND

Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable.

OBJECTIVE

This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence.

METHODS

A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy.

RESULTS

The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences.

CONCLUSIONS

We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.

The effect of pecha-kucha training on fear and belief in myths of COVID-19 in elderly women

Adequate and accurate information reduces pandemic fear in elderly women with chronic disease, one of the risk groups for COVID-19. We aim to determine the effect of pecha kucha pandemic training on the fear and belief in myths of COVID-19 in elderly women. This prospective, randomized controlled experimental study with pre- and post-tests employed a total of 64 elderly women, including 32 for each of experimental and control groups. The data were collected using an introductory information form, the Questionnaire for Beliefs in COVID-19 Myths, and the Fear of COVID-19 Scale. Women in the experimental group were informed about COVID-19, using a pecha kucha presentation via smart phone. Those in the control group were given the same information using classical lecture method. The data were collected before, just after, and 3 months after the training and analyzed using Pearson's chi-square, Mann-Whitney U, Friedman, Wilcoxon Signed Ranks tests. Elderly women in the experimental group had significantly lower fear and belief in myths of COVID-19 both just after and 3 months after the training (p < 0.05, p < 0.05). A pandemic training by pecha-kucha presentation, which is a short, clear, understandable and memorable method of teaching, can reduce both fear and belief in myths of COVID-19 in elderly women.

ARIMA model forecasting analysis of the prices of multiple vegetables under the impact of the COVID-19

As a large agricultural country, China’s vegetable prices affect the increase in production and income of farmers and the daily life of urban and rural residents and influence the healthy development of Chinese agriculture. 51,567 vegetable price data of 2020 are analyzed to determine the factors that influence vegetable price fluctuations in two dimensions (vertical and horizontal) in the special context of the COVID-19, and an ARIMA model of short-term price prediction is then employed and evaluated. Based on the factors affecting vegetable prices, the results of the model are further examined. Finally, pertinent suggestions are made for the development of the local vegetable industry in the post-epidemic era.

New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China

Background

In the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic.

Methods

Based on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics.

Results

Research findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors.

Conclusion

The study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.

The psychological reassurance effect of mobile tracing apps in Covid-19 Era

As part of their public health policies, most countries have launched mobile tracing applications (apps) to reduce the spread of the COVID-19 virus and reassure their citizens. To the best of our knowledge, no study has explored the importance of 'well-being' and 'trust in the future' in the context of digital contact-tracing apps. This is an important gap, especially given the importance of citizens' acceptance of a mobile tracing app and its role in reassuring citizens. Therefore, we study the French government's tracing app-StopCovid-as experienced by a sample of 832 participants from France. The results establish strong links between perceived value and trust in government, well-being, and trust in the future, which are considered the key features of the reassurance effect in a pandemic context. In addition, a multigroup analysis (MGA) allows us to compare the effect of several moderators on the overall model, such as the users versus nonusers of tracking apps or infected versus noninfected with COVID-19. The study provides practical implications by highlighting how governments should deploy mobile tracing apps to contribute to public health and reassure their citizens during the pandemic.

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19

With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.

COVID19-HPSMP : COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction ( COVID19 PRIMO ) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction ( COVID19-HPSMP ), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

An Evaluation of a Microlearning Intervention to Limit COVID-19 Online Misinformation

As part of a design-based research project, we designed, developed, and evaluated a web-based microlearning intervention in the form of a comic into the problem of COVID-19 online misinformation. In this paper, we report on our formative evaluation efforts. Specifically, we assessed the degree to which the comic was effective and engaging via responses to a questionnaire (n = 295) in a posttest-only non-experimental design. The intervention focused on two learning objectives, aiming to enable users to recognize (a) that online misinformation is often driven by strong emotions like fear and anger, and (b) that one strategy for disrupting the spread of misinformation can be the act of stopping before reacting to misinformation. Results indicate that the comic was both effective and engaging in achieving these learning objectives.

Medical and Health-related Misinformation on Social Media: Analysis of the Scientific Literature

Background

Social media has been extensively used for the communication of health-related information and consecutively for the potential spread of medical misinformation. Conventional systematic reviews have been published on this topic to identify original articles and to summarize their methodological approaches and themes. A bibliometric study could complement their findings, for instance, by evaluating the geographical distribution of the publications and determining if they were well cited and disseminated in high-impact journals.

Objective

The aim of this study was to perform a bibliometric analysis of the current literature to discover the prevalent trends and topics related to medical misinformation on social media.

Methods

The Web of Science Core Collection electronic database was accessed to identify relevant papers with the following search string: ALL=(misinformati* OR “wrong informati*” OR disinformati* OR “misleading informati*” OR “fake news*”) AND ALL=(medic* OR illness* OR disease* OR health* OR pharma* OR drug* OR therap*) AND ALL=(“social media*” OR Facebook* OR Twitter* OR Instagram* OR YouTube* OR Weibo* OR Whatsapp* OR Reddit* OR TikTok* OR WeChat*). Full records were exported to a bibliometric software, VOSviewer, to link bibliographic information with citation data. Term and keyword maps were created to illustrate recurring terms and keywords.

Results

Based on an analysis of 529 papers on medical and health-related misinformation on social media, we found that the most popularly investigated social media platforms were Twitter (n=90), YouTube (n=67), and Facebook (n=57). Articles targeting these 3 platforms had higher citations per paper (>13.7) than articles covering other social media platforms (Instagram, Weibo, WhatsApp, Reddit, and WeChat; citations per paper <8.7). Moreover, social media platform–specific papers accounted for 44.1% (233/529) of all identified publications. Investigations on these platforms had different foci. Twitter-based research explored cyberchondria and hypochondriasis, YouTube-based research explored tobacco smoking, and Facebook-based research studied vaccine hesitancy related to autism. COVID-19 was a common topic investigated across all platforms. Overall, the United States contributed to half of all identified papers, and 80% of the top 10 most productive institutions were based in this country. The identified papers were mostly published in journals of the categories public environmental and occupational health, communication, health care sciences services, medical informatics, and medicine general internal, with the top journal being the Journal of Medical Internet Research.

Conclusions

There is a significant platform-specific topic preference for social media investigations on medical misinformation. With a large population of internet users from China, it may be reasonably expected that Weibo, WeChat, and TikTok (and its Chinese version Douyin) would be more investigated in future studies. Currently, these platforms present research gaps that leave their usage and information dissemination warranting further evaluation. Future studies should also include social platforms targeting non-English users to provide a wider global perspective.

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts

In order to improve the prediction accuracy of COVID-19 and strengthen the economic management and control, a self-correcting intelligent pandemic prediction model is proposed. The research shows that: (1) The pandemic, as a major social factor, has a great impact on the consumption expenditure level of various industries, and directly affects the public consumption expenditure level in different periods for example the spend_all in California decreased by 37.7%; (2) The economic losses caused by the increasingly serious pandemic period far less than the economic losses caused by the panic in the early stage of the pandemic, and the reason is the government's strong guarantee policies stimulate economic recovery. For example, the spend_all in California has increased from -37.7% to about -18%; (3) The proposed model improves the prediction accuracy of economic trend, and the government can make prediction according to the early warning economic prediction, which provides reference for the economic management control at the micro level of enterprises and the macro level of the nation; (4) The dual strategies of self correcting prediction and pandemic control realize the overall design of real-time control and performance optimization of economic process, and provide reference for the overall recovery of the economy.

Rethinking Fake News: Disinformation and Ideology during the time of COVID-19 Global Pandemic

Digital media and citizen journalism has escalated the infiltration of fake news attempting to create a post truth society (Lazer et al., 2018). The COVID-19 pandemic has seen a surge of misinformation leading to anti-mask, anti-vaccine and anti-5G protests on a global scale. Although the term ‘misinformation’ has been generalized in media and scholarly work, there is a fundamental difference between how misinformation impacts society, compared to more strategically planned disinformation attacks. In this study we explore the ideological constructs of citizens towards acceptance or rejection of disinformation during the heightened time of a COVID-19 global health crisis. Our analysis follows two specific disinformation propagandas evaluated through social network analysis of Twitter data in addition to qualitative insights generated from tweets and in-depth interviews.

Exploring the Interpretation of COVID-19 Messaging on Older Adults’ Experiences of Vulnerability

Public health messages and societal discourse during the COVID-19 pandemic have consistently indicated a higher morbidity and mortality risk for older people, particularly those with multiple health conditions. Older adults’ interpretations of pandemic messaging can shape their perceived vulnerability and behaviours. This study examined their perspectives on COVID-19 messaging. Eighteen community-dwelling older adults residing in Manitoba (Canada) participated in semi-structured telephone interviews between July and August 2020, a period of low COVID-19 cases within the province. Inductive thematic analysis was used to identify key themes that described participants’ processes of information interpretation when consuming pandemic-related messages, their emotional responses to messaging and consequent vulnerability, and the impacts of messaging on their everyday lives. Understanding how older adults have construed COVID-19 and pandemic-related messages, and the subsequent impact on their daily behaviours, is the first step towards shaping societal discourse and sets the stage for examining the pandemic’s long-term effects.

Human information behavior during the Covid-19 health crisis. A literature review

The research carried out on human information behavior (HIB) during the Covid-19 health crisis was reviewed, with the premise that HIB and information practices allow humans to adapt to the changing circumstances of existence. A literature search was run on the LISTA and Google Scholar databases from middle March 2020 up to the end of March 2021. After filtering retrieved results, 52 studies were selected. Results are summarized into seven main themes, including the use of traditional and social media, infoveillance of search engines and social media activity, misinformation, disinformation and infodemics, and uncertainty and emotions. Results point to the need to carry out additional research in specific contexts and addressing vulnerable and marginalized groups. Further areas of inquiry include the interplay of emotions, knowledge and behaviors during the information seeking process, a better understanding of local knowledge and experiential knowledge, and the need to comprehend the limitations of ICT.

Sustainability Analysis of a ZnO-NaCl-Based Capacitor Using Accelerated Life Testing and an Intelligent Modeling Approach

From small toys to satellites, capacitors play a vital role as an energy storage element, filtering or controlling other critical tasks. This research paper focuses on estimating the remaining useful life of a nanocomposite-based fabricated capacitor using various experimental and artificial intelligence techniques. Accelerated life testing is used to explore the sustainability and remaining useful life of the fabricated capacitor. The acceleration factors affecting the health of capacitors are investigated, and experiments are designed using Taguchi’s approach. The remaining useful lifetime of the fabricated capacitor is calculated using a statistical technique, i.e., regression analysis using Minitab 18.1 software. An expert model is designed using artificial neural networks (ANN), which warns the user of any upcoming faults and failures. The average remaining useful life of the fabricated capacitor, using accelerated life testing, regression, and artificial neural network, is reported as 13,724.3 h, 14,515.9 h, and 14,247.1 h, respectively. A comparison analysis is conducted, and performance metrics are analyzed to opt for the most efficient technique for the prediction of the remaining useful life of the fabricated capacitor, which confirms 93.83% accuracy using the statistical method and 95.82% accuracy using artificial neural networks. The root mean square error (RMSE) of regression and artificial neural networks is found to be 0.102 and 0.167, respectively, which validates the consistency of the reliability methods.

Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda.

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

Accurate and fast detection of COVID-19 patients is crucial to control this pandemic. Due to the scarcity of COVID-19 testing kits, especially in developing countries, there is a crucial need to rely on alternative diagnosis methods. Deep learning architectures built on image modalities can speed up the COVID-19 pneumonia classification from other types of pneumonia. The transfer learning approach is better suited to automatically detect COVID-19 cases due to the limited availability of medical images. This paper introduces an Optimized Transfer Learning-based Approach for Automatic Detection of COVID-19 (OTLD-COVID-19) that applies an optimization algorithm to twelve CNN architectures to diagnose COVID-19 cases using chest x-ray images. The OTLD-COVID-19 approach adapts Manta-Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters' values of the CNN architectures to improve their classification performance. The proposed dataset is collected from eight different public datasets to classify 4-class cases (COVID-19, pneumonia bacterial, pneumonia viral, and normal). The experimental result showed that DenseNet121 optimized architecture achieves the best performance. The evaluation results based on Loss, Accuracy, F1-score, Precision, Recall, Specificity, AUC, Sensitivity, IoU, and Dice values reached 0.0523, 98.47%, 0.9849, 98.50%, 98.47%, 99.50%, 0.9983, 0.9847, 0.9860, and 0.9879 respectively.