Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning

Vaccination uptake is influenced by many cues during health information seeking online

BACKGROUND

Much government response to improving vaccination uptake during the COVID-19 pandemic has focused on the problems of misinformation and disinformation. There may, however, be other signals within online health information that influence uptake of vaccination.

OBJECTIVE

This study identified the influence of various health information signals within online information communities on the intention of receiving the vaccine.

METHOD

A deductive approach was used to derive constructs from signalling theory. Constructs were validated by a convenience sample using a questionnaire. Structural equation modelling (SEM) was used to evaluate the measurement model, the structural model and the multigroup analysis.

RESULTS

The analysis showed a significant impact of signals derived from past experience, information asymmetry and source credibility constructs on the perceived quality of the vaccine service. The perceived quality also had a significant impact on the intention to receive the vaccine.

DISCUSSION

Signalling theory was able to explain the importance of health information signals perceived from online platforms on the intention of individuals to receive the vaccine.

CONCLUSION

Information asymmetry between information provider and receiver, perceived credibility of sources and perceived quality of the vaccination service may influence decisions about vaccination.

Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube

This study explores how individuals who have experienced COVID-19 share their stories on YouTube, focusing on the nature of information disclosure, public engagement, and emotional impact pertaining to consumer health. Using a dataset of 186 YouTube videos, we used text mining and video analytics techniques to analyze textual transcripts and visual frames to identify themes, emotions, and their relationship with viewer engagement metrics. Findings reveal eight key themes: infection origins, symptoms, treatment, mental well-being, isolation, prevention, government directives, and vaccination. While viewers engaged most with videos about infection origins, treatment, and vaccination, fear and sadness in the text consistently drove views, likes, and comments. Visuals primarily conveyed happiness and sadness, but their influence on engagement varied. This research highlights the crucial role YouTube plays in disseminating COVID-19 patient narratives and suggests its potential for improving health communication strategies. By understanding how emotions and content influence viewer engagement, healthcare professionals and public health officials can tailor their messaging to better connect with the public and address pandemic-related anxieties.

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Background and Aims

This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis.

Methods

On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements.

Results

After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms.

Conclusion

The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.

Evaluation of online videos to engage viewers and support decision-making for COVID-19 vaccination: how narratives and race/ethnicity enhance viewer experiences

Background

Vaccine hesitancy has hampered the control of COVID-19 and other vaccine-preventable diseases.

Methods

We conducted a national internet-based, quasi-experimental study to evaluate COVID-19 vaccine informational videos. Participants received an informational animated video paired with the randomized assignment of (1) a credible source (differing race/ethnicity) and (2) sequencing of a personal narrative before or after the video addressing their primary vaccine concern. We examined viewing time and asked video evaluation questions to those who viewed the full video.

Results

Among 14,235 participants, 2,422 (17.0%) viewed the full video. Those who viewed a personal story first (concern video second) were 10 times more likely to view the full video (p < 0.01). Respondent-provider race/ethnicity congruence was associated with increased odds of viewing the full video (aOR: 1.89, p < 0.01). Most viewers rated the informational video(s) to be helpful, easy to understand, trustworthy, and likely to impact others' vaccine decisions, with differences by demographics and also vaccine intentions and concerns.

Conclusion

Using peer-delivered, personal narrative, and/or racially congruent credible sources to introduce and deliver vaccine safety information may improve the openness of vaccine message recipients to messages and engagement.

Shockwavelike Behavior across Social Media

Online communities featuring "anti-X" hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwavelike solutions explain how, why, and when such activity rises from "out-of-nowhere," and show how it can be delayed, reshaped, and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.

Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign

BACKGROUND

The COVID-19 pandemic was accompanied by an "infodemic"-an overwhelming excess of accurate, inaccurate, and uncertain information. The social media-based science communication campaign Dear Pandemic was established to address the COVID-19 infodemic, in part by soliciting submissions from readers to an online question box. Our study characterized the information needs of Dear Pandemic's readers by identifying themes and longitudinal trends among question box submissions.

METHODS

We conducted a retrospective analysis of questions submitted from August 24, 2020, to August 24, 2021. We used Latent Dirichlet Allocation topic modeling to identify 25 topics among the submissions, then used thematic analysis to interpret the topics based on their top words and submissions. We used t-Distributed Stochastic Neighbor Embedding to visualize the relationship between topics, and we used generalized additive models to describe trends in topic prevalence over time.

RESULTS

We analyzed 3839 submissions, 90% from United States-based readers. We classified the 25 topics into 6 overarching themes: 'Scientific and Medical Basis of COVID-19,' 'COVID-19 Vaccine,' 'COVID-19 Mitigation Strategies,' 'Society and Institutions,' 'Family and Personal Relationships,' and 'Navigating the COVID-19 Infodemic.' Trends in topics about viral variants, vaccination, COVID-19 mitigation strategies, and children aligned with the news cycle and reflected the anticipation of future events. Over time, vaccine-related submissions became increasingly related to those surrounding social interaction.

CONCLUSIONS

Question box submissions represented distinct themes that varied in prominence over time. Dear Pandemic's readers sought information that would not only clarify novel scientific concepts, but would also be timely and practical to their personal lives. Our question box format and topic modeling approach offers science communicators a robust methodology for tracking, understanding, and responding to the information needs of online audiences.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

BACKGROUND

Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE

We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS

We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS

A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.

CONCLUSIONS

The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Towards a soft three-level voting model (Soft T-LVM) for fake news detection

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and result in a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to December 31st, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. After calculating the number of topics, Latent Dirichlet Allocation (LDA) model is used to extract topics and keywords. We find that people had different sentiments between Chinese vaccine and those in other countries, and the sentiment value might be affected by the number of daily news cases and deaths, and the nature of key issues in the communication network, as well as revealing the intensity and evolution of 10 topics of major public concern, and provides insights into vaccine trust.

The use of the Dark Web as a COVID-19 information source: A three-country study

The Dark Web (i.e., the anonymous web or Darknet) contains potentially harmful COVID-19-related information and content such as conspiracy theories and forged certificates. The Dark Web may particularly attract individuals who are suspicious about the pandemic, but there is no research concerning the use of the Dark Web as a COVID-19 information source. In this study, we investigated the role of COVID-19 skepticism, online activities, and loneliness in the use of the Dark Web platforms as a COVID-19 information source. The data (N = 3000) were gathered in April 2021 from 18 to 75-year-old respondents from Finland (n = 1000), Sweden (n = 1000) and the United Kingdom (n = 1000). The respondents were asked how often they had utilized Dark Web platforms (for example via TOR-network) as a COVID-19 information source during the pandemic. Self-reported measures of institutional trust, anti-vaccine stances, restriction obedience, online activities, and loneliness were used as predictors in the logistic regression model. Age, gender, and education were also included in the model. The Dark Web use was more prevalent in the UK and Sweden. There was an association between anti-vaccine stances and active Dark Web use in the UK and Sweden, while low institutional trust predicted use among Finnish respondents. In all countries, restriction disobedience was related to Dark Web use as a COVID-19 information source. Frequent online gambling, increased social media use, and loneliness predicted Dark Web use, and these associations were even stronger among frequent Dark Web users than occasional users. Younger age and male gender were also associated with Dark Web use. The unregulated nature of the Dark Web makes it a risky alternative to COVID-19 information, attracting individuals who are suspicious about the pandemic and overall active online users. Misleading information and availability of forged certificates on the Dark Web challenge official health policies, posing significant risks for both individual and public health.

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.

Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.

Covid-19 fake news sentiment analysis

'Fake news' refers to the misinformation presented about issues or events, such as COVID-19. Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously, however, they have been ineffectual. This research uses Information Fusion to obtain real news data from News Broadcasting, Health, and Government websites, while fake news data are collected from social media sites. 39 features were created from multimedia texts and used to detect fake news regarding COVID-19 using state-of-the-art deep learning models. Our model's fake news feature extraction improved accuracy from 59.20% to 86.12%. Overall high precision is 85% using the Recurrent Neural Network (RNN) model; our best recall and F1-Measure for fake news were 83% using the Gated Recurrent Units (GRU) model. Similarly, precision, recall, and F1-Measure for real news are 88%, 90%, and 88% using the GRU, RNN, and Long short-term memory (LSTM) model, respectively. Our model outperformed standard machine learning algorithms.

COVID-19 vaccine hesitancy: a social media analysis using deep learning

Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.

Sentinel node approach to monitoring online COVID-19 misinformation

Understanding how different online communities engage with COVID-19 misinformation is critical for public health response. For example, misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter. Our approach provides a means to examine the breadth of misinformation engagement using modest data needs and computational resources. We identify a subset of accounts from different Twitter communities discussing COVID-19, and follow these 'sentinel nodes' longitudinally from July 2020 to January 2021. We characterize sentinel nodes in terms of a linked domain preference score, and use a standardized similarity score to examine alignment of tweets within and between communities. We find that media preference is strongly correlated with the amount of misinformation propagated by sentinel nodes. Engagement with sensationalist misinformation topics is largely confined to a cluster of sentinel nodes that includes influential conspiracy theorist accounts. By contrast, misinformation relating to COVID-19 severity generated widespread engagement across multiple communities. Our findings indicate that misinformation downplaying COVID-19 severity is of particular concern for public health response. We conclude that the sentinel node approach can be an effective way to assess breadth and depth of online misinformation penetration.

Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature

Background

Vaccine hesitancy continues to limit global efforts in combatting the COVID-19 pandemic. Emerging research demonstrates the role of social media in disseminating information and potentially influencing people's attitudes towards public health campaigns. This systematic review sought to synthesize the current evidence regarding the potential role of social media in shaping COVID-19 vaccination attitudes, and to explore its potential for shaping public health interventions to address the issue of vaccine hesitancy.

Methods

We performed a systematic review of the studies published from inception to 13 of March2022 by searching PubMed, Web of Science, Embase, PsychNET, Scopus, CINAHL, and MEDLINE. Studies that reported outcomes related to coronavirus disease 2019 (COVID-19) vaccine (attitudes, opinion, etc.) gathered from the social media platforms, and those analyzing the relationship between social media use and COVID-19 hesitancy/acceptance were included. Studies that reported no outcome of interest or analyzed data from sources other than social media (websites, newspapers, etc.) will be excluded. The Newcastle Ottawa Scale (NOS) was used to assess the quality of all cross-sectional studies included in this review. This study is registered with PROSPERO (CRD42021283219).

Findings

Of the 2539 records identified, a total of 156 articles fully met the inclusion criteria. Overall, the quality of the cross-sectional studies was moderate - 2 studies received 10 stars, 5 studies received 9 stars, 9 studies were evaluated with 8, 12 studies with 7,16 studies with 6, 11 studies with 5, and 6 studies with 4 stars. The included studies were categorized into four categories. Cross-sectional studies reporting the association between reliance on social media and vaccine intentions mainly observed a negative relationship. Studies that performed thematic analyses of extracted social media data, mainly observed a domination of vaccine hesitant topics. Studies that explored the degree of polarization of specific social media contents related to COVID-19 vaccines observed a similar degree of content for both positive and negative tone posted on different social media platforms. Finally, studies that explored the fluctuations of vaccination attitudes/opinions gathered from social media identified specific events as significant cofactors that affect and shape vaccination intentions of individuals.

Interpretation

This thorough examination of the various roles social media can play in disseminating information to the public, as well as how individuals behave on social media in the context of public health events, articulates the potential of social media as a platform of public health intervention to address vaccine hesitancy.

Funding

None.

Using supervised learning to analyze the French vaccine debate on Twitter

Background

As the COVID-19 pandemic progressed, disinformation, fake news, and conspiracy theories spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media posts is particularly important, but the large amount of data exchanged on social media platforms requires specific methods. This is why machine learning and natural language processing models are increasingly applied to social media data.

Objective

The aim of this study is to examine the capability of the CamemBERT French-language model to faithfully predict the elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic, or irrelevant to the studied topic.

Methods

A total of 901,908 unique French-language tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using Twitter’s application programming interface (version 2; Twitter Inc). Approximately 2000 randomly selected tweets were labeled with 2 types of categorizations: (1) arguments for (pros) or against (cons) vaccination (health measures included) and (2) type of content (scientific, political, social, or vaccination status). The CamemBERT model was fine-tuned and tested for the classification of French-language tweets. The model’s performance was assessed by computing the F1-score, and confusion matrices were obtained.

Results

The accuracy of the applied machine learning reached up to 70.6% for the first classification (pro and con tweets) and up to 90% for the second classification (scientific and political tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio 1.86; 95% CI 1.20-2.86).

Conclusions

The accuracy of the model is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the model drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy by selecting tweets using a new method based on tweet length.

The Vaccination Concerns in COVID-19 Scale (VaCCS): Development and validation

Vaccines are highly effective in minimizing serious cases of COVID-19 and pivotal to managing the COVID-19 pandemic. Despite widespread availability, vaccination rates fall short of levels required to bring about widespread immunity, with low rates attributed to vaccine hesitancy. It is therefore important to identify the beliefs and concerns associated with vaccine intentions and uptake. The present study aimed to develop and validate, using the AMEE Guide, the Vaccination Concerns in COVID-19 Scale (VaCCS), a comprehensive measure of beliefs and concerns with respect to COVID-19 vaccines. In the scale development phase, samples of Australian (N = 53) and USA (N = 48) residents completed an initial open-response survey to elicit beliefs and concerns about COVID-19 vaccines. A concurrent rapid literature review was conducted to identify content from existing scales on vaccination beliefs. An initial pool of items was developed informed by the survey responses and rapid review. The readability and face validity of the item pool was assessed by behavioral science experts (N = 5) and non-experts (N = 10). In the scale validation phase, samples of Australian (N = 522) and USA (N = 499) residents completed scaled versions of the final item pool and measures of socio-political, health beliefs and outcomes, and trait measures. Exploratory factor analysis yielded a scale comprising 35 items with 8 subscales, and subsequent confirmatory factor analyses indicated acceptable fit of the scale structure with the data in each sample and factorial invariance across samples. Concurrent and predictive validity tests indicated a theoretically and conceptually predictable pattern of relations between the VaCCS subscales with the socio-political, health beliefs and outcomes, and trait measures, and key subscales predicted intentions to receive the COVID-19 vaccine. The VaCCS provides a novel measure to assess beliefs and concerns toward COVID-19 vaccination that researchers and practitioners can use in its entirety or select specific sub-scales to use according to their needs.

Review on COVID-19 diagnosis models based on machine learning and deep learning approaches

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

Perceptions and Feelings of Brazilian Health Care Professionals Regarding the Effects of COVID-19: Cross-sectional Web-Based Survey

BACKGROUND

The importance of health professionals has been recognized in COVID-19 pandemic-affected countries, especially in those such as Brazil, which is one of the top 3 countries that have been affected in the world. However, the workers' perception of the stress and the changes that the pandemic has caused in their lives vary according to the conditions offered by these affected countries, including salaries, individual protection equipment, and psychological support.

OBJECTIVE

The purpose of this study was to identify the perceptions of Brazilian health workers regarding the COVID-19 pandemic impact on their lives, including possible self-contamination and mental health.

METHODS

This cross-sectional web-based survey was conducted in Brazil by applying a 32-item questionnaire, including multiple-choice questions by using the Google Forms electronic assessment. This study was designed to capture spontaneous perceptions from health professionals. All questions were mandatory and divided into 2 blocks with different proposals: personal profile and COVID-19 pandemic impact.

RESULTS

We interviewed Brazilian health professionals from all 5 Brazilian regions (N=1376). Our study revealed that 1 in 5 (23%) complained about inadequate personal protective equipment, including face shields (234/1376, 17.0%), masks (206/1376, 14.9%), and laboratory coats (138/1376, 10.0%), whereas 1 in 4 health professionals did not have enough information to protect themselves from the coronavirus disease. These professionals had anxiety due to COVID-19 (604/1376, 43.9%), difficulties in sleep (593/1376, 43.1%), and concentrating on work (453/1376, 32.9%). Almost one-third experienced traumatic situations at work (385/1376, 28.0%), which may have led to negative feelings of fear of COVID-19 and sadness. Despite this situation, there was hope and empathy among their positive feelings. The survey also showed that 1 in 5 acquired COVID-19 with the most classic and minor symptoms, including headache (274/315, 87.0%), body pain (231/315, 73.3%), tiredness (228/315, 72.4%), and loss of taste and smell (208/315, 66.0%). Some of their negative feelings were higher than those of noninfected professionals (fear of COVID-19, 243/315, 77.1% vs 509/1061, 48.0%; impotence, 142/315, 45.1% vs 297/1061, 28.0%; and fault, 38/315, 12.1% vs 567/1061, 53.4%, respectively). Another worrying outcome was that 61.3% (193/315) reported acquiring an infection while working at a health facility and as expected, most of the respondents felt affected (344/1376, 25.0%) or very affected (619/1376, 45.0%) by the COVID-19.

CONCLUSIONS

In Brazil, the health professionals were exposed to a stressful situation and to the risk of self-contamination-conditions that can spell future psychological problems for these workers. Our survey findings showed that the psychological support for this group should be included in the future health planning of Brazil and of other hugely affected countries to assure a good mental health condition for the medical teams in the near future.

Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms

We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.

Hidden order across online extremist movements can be disrupted by nudging collective chemistry

Disrupting the emergence and evolution of potentially violent online extremist movements is a crucial challenge. Extremism research has analyzed such movements in detail, focusing on individual- and movement-level characteristics. But are there system-level commonalities in the ways these movements emerge and grow? Here we compare the growth of the Boogaloos, a new and increasingly prominent U.S. extremist movement, to the growth of online support for ISIS, a militant, terrorist organization based in the Middle East that follows a radical version of Islam. We show that the early dynamics of these two online movements follow the same mathematical order despite their stark ideological, geographical, and cultural differences. The evolution of both movements, across scales, follows a single shockwave equation that accounts for heterogeneity in online interactions. These scientific properties suggest specific policies to address online extremism and radicalization. We show how actions by social media platforms could disrupt the onset and 'flatten the curve' of such online extremism by nudging its collective chemistry. Our results provide a system-level understanding of the emergence of extremist movements that yields fresh insight into their evolution and possible interventions to limit their growth.

CoVerifi: A COVID-19 news verification system

There is an abundance of misinformation, disinformation, and “fake news” related to COVID-19, leading the director-general of the World Health Organization to term this an ‘infodemic’. Given the high volume of COVID-19 content on the Internet, many find it difficult to evaluate veracity. Vulnerable and marginalized groups are being misinformed and subject to high levels of stress. Riots and panic buying have also taken place due to “fake news”. However, individual research-led websites can make a major difference in terms of providing accurate information. For example, the Johns Hopkins Coronavirus Resource Center website has over 81 million entries linked to it on Google. With the outbreak of COVID-19 and the knowledge that deceptive news has the potential to measurably affect the beliefs of the public, new strategies are needed to prevent the spread of misinformation. This study seeks to make a timely intervention to the information landscape through a COVID-19 “fake news”, misinformation, and disinformation website. In this article, we introduce CoVerifi, a web application which combines both the power of machine learning and the power of human feedback to assess the credibility of news. By allowing users the ability to “vote” on news content, the CoVerifi platform will allow us to release labelled data as open source, which will enable further research on preventing the spread of COVID-19-related misinformation. We discuss the development of CoVerifi and the potential utility of deploying the system at scale for combating the COVID-19 “infodemic”.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

BACKGROUND

SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

OBJECTIVE

The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

METHODS

A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

RESULTS

The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

CONCLUSIONS

In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Applications of artificial intelligence in battling against covid-19: A literature review

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

Considering Emotion in COVID-19 Vaccine Communication: Addressing Vaccine Hesitancy and Fostering Vaccine Confidence

Long-term control of the COVID-19 pandemic hinges in part on the development and uptake of a preventive vaccine. In addition to a segment of population that refuses vaccines, the novelty of the disease and concerns over safety and efficacy of the vaccine have a sizable proportion of the U.S. indicating reluctance to getting vaccinated against COVID-19. Among various efforts to address vaccine hesitancy and foster vaccine confidence, evidence-based communication strategies are critical. There are opportunities to consider the role of emotion in communication efforts. In this commentary, we highlight several ways negative as well as positive emotions may be considered and leveraged. Examples include attending to negative emotions such as fear and anxiety, raising awareness of emotional manipulations by anti-vaccine disinformation efforts, and activating positive emotions such as altruism and hope as part of vaccine education endeavors.

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.