Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset

Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation

BACKGROUND

Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations.

OBJECTIVE

This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms.

METHODS

We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization's (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends.

RESULTS

We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines.

CONCLUSIONS

Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

Comparison of Impressions of COVID-19 Vaccinations Stratified by the Number of Vaccinations Among Japanese Healthcare Professional University Students

OBJECTIVE

Coronavirus infectious disease, that emerged in 2019 (COVID-19) has been a major public health issue not only in Japan, but also worldwide, and the implementation of a proper vaccination strategy has been important. To promote vaccination, the present study compared impressions of COVID-19 vaccinations stratified by the number of vaccinations among healthcare professional university students in Okayama, Japan, and suggests better vaccination strategies.

METHOD

A total of 212 Japanese healthcare professional university students were enrolled in this clinical qualitative study using the text mining method. A self-reported questionnaire, including questions such as "What do you think about COVID-19 vaccinations?" was performed. We also examined the number of vaccinations, sex, history of COVID-19 infection, and daily mask use.

RESULTS

A total of 5,935 words were obtained and "Think" (169 times) was the most frequently used followed by "Inject" (108 times), "Inoculation" (97 times), "Vaccine" (83 times), "Corona" (66 times) and "Side effects" (49 times). Characteristic words were "Safety" in non-vaccinated subjects and "Side effects" and "Necessary" in vaccinated subjects. In addition, "Safety" in non-vaccinated men and "Frightening" in non-vaccinated women were characteristic and fundamental features.

CONCLUSION

Impressions of COVID-19 vaccinations stratified by the number of vaccinations differed among healthcare professional university students. The provision of appropriate information on safety to non-vaccinated subjects and side effects to vaccinated subjects appears to be necessary. In addition, sex-specific information may be required for non-vaccinated subjects.

Addressing bias in artificial intelligence for public health surveillance

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance.

Identification, analysis and prediction of valid and false information related to vaccines from Romanian tweets

Introduction

The online misinformation might undermine the vaccination efforts. Therefore, given the fact that no study specifically analyzed online vaccine related content written in Romanian, the main objective of the study was to detect and evaluate tweets related to vaccines and written in Romanian language.

Methods

1,400 Romanian vaccine related tweets were manually classified in true, neutral and fake information and analyzed based on wordcloud representations, a correlation analysis between the three classes and specific tweet characteristics and the validation of several predictive machine learning algorithms.

Results and discussion

The tweets annotated as misinformation showed specific word patterns and were liked and reshared more often as compared to the true and neutral ones. The validation of the machine learning algorithms yielded enhanced results in terms of Area Under the Receiver Operating Characteristic Curve Score (0.744-0.843) when evaluating the Support Vector Classifier. The predictive model estimates in a well calibrated manner the probability that a specific Twitter post is true, neutral or fake. The current study offers important insights regarding vaccine related online content written in an Eastern European language. Future studies must aim at building an online platform for rapid identification of vaccine misinformation and raising awareness for the general population.

Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches.

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the 'bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Social media platforms, such as Twitter, have been paramount in the COVID-19 context due to their ability to collect public concerns about the COVID-19 vaccination campaign, which has been underway to end the COVID-19 pandemic. This worldwide campaign has heavily relied on the actual willingness of individuals to get vaccinated independently of the language they speak or the country they reside. This study analyzes Twitter posts about Pfizer/BioNTech, Moderna, AstraZeneca/Vaxzevria, and Johnson & Johnson vaccines by considering the most spoken western languages. Tweets were sampled between April 15 and September 15, 2022, after the injections of at least three doses, collecting 9,513,063 posts that contained vaccine-related keywords. To determine the success of vaccination, temporal and sentiment analysis have been conducted, reporting opinion changes over time and their corresponding events whenever possible concerning each vaccine. Furthermore, we have extracted the main topics over languages providing potential bias due to the language-specific dictionary, such as Moderna in Spanish, and grouped them per country. Once performed the pre-processed procedure we worked with 8,343,490 tweets. Our findings show that Pfizer has been the most debated vaccine worldwide, and the main concerns have been the side effects on pregnant women and children and heart diseases.

Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.

PURPOSE

To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.

METHODS

The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).

RESULTS

The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).

CONCLUSION

The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data

Objectives

Vaccination is widely regarded as the paramount approach for safeguarding individuals against the repercussions of COVID-19. Nonetheless, concerns surrounding the efficacy and potential adverse effects of these vaccines have become prevalent among the public. To date, there has been a paucity of research investigating public perceptions and the adoption of COVID-19 vaccines. Therefore, the present study endeavours to address this lacuna by undertaking a spatiotemporal analysis of sentiments towards vaccination and its uptake in England at the local authority level, while concurrently examining the sociodemographic attributes at the national level.

Methods

A sentiment analysis of Twitter data was undertaken to delineate the distribution of positive sentiments and their demographic correlates. Positive sentiments were categorized into clusters to streamline comparison across different age and gender demographics. The relationship between positive sentiment and vaccination uptake was evaluated using Spearman's correlation coefficient. Additionally, a bivariate analysis was carried out to further probe public sentiment towards COVID-19 vaccines and their local adoption rates.

Result

The results indicated that the majority of positive tweets were posted by males, although females expressed higher levels of positive sentiment. The age group over 40 dominated the positive tweets and exhibited the highest sentiment polarity. Additionally, vaccination uptake was positively correlated with the number of positive tweets and the age group at the local authority level.

Conclusion

Overall, public opinions on COVID-19 vaccines are predominantly positive. The number of individuals receiving vaccinations at the local authority level is positively correlated with the prevalence of positive attitudes towards vaccines, particularly among the population aged over 40. These findings suggest that targeted efforts to increase vaccination uptake among younger populations, particularly males, are necessary to achieve widespread vaccination coverage.

Influencing factors of hospital-acquired COVID-19 prevention and control status among emergency support frontline healthcare workers under closed-loop management: a cross-sectional study

Background

This study aimed to understand the hospital-acquired COVID-19 infection rate and infection prevention and control status of emergency support frontline healthcare workers (ESFHCWs) under closed-loop management, and to explore the related factors affecting hospital-acquired COVID-19 prevention and control status.

Methods

The study site was a provincial-level tertiary hospital in the Xinjiang Uygur Autonomous Region specializing in treating COVID-19 patients. ESFHCWs were assigned from different hospitals in Zhejiang Province to provide emergency medical support in this specialized hospital. All ESFHCWs were managed using a closed loop. A self-designed questionnaire was used to estimate basic information, work experience, and the status of infection prevention and control (SIPC). A total of 269 ESFHCWs responded to the questionnaire. A generalized linear regression model was used to estimate the factors influencing SIPC.

Results

There were six hospital-acquired COVID-19 cases, with an infection rate of 2.23%. The independent risk factors influencing COVID-19 prevention and control status were work seniority, anxiety disorder, and consumption of gastrointestinal, anti-inflammatory and anti-asthmatic, and hypnotic sedative drugs. Compared with ESFHCWs with more than 10 years of work seniority, ESFHCWs with less than 5 years of work seniority and 5-10 years of work seniority had lower COVID-19 SIPC scores. Among ESFHCWs with anxiety disorder, the SIPC score was significantly lower than that of ESFHCWs without anxiety disorder. The SIPC scores of ESFHCWs taking other medications (gastrointestinal, anti-inflammatory and anti-asthmatic, and hypnotic sedative drugs) were lower than those of ESFHCWs who did not.

Conclusion

The closed-loop management method may be effective in reducing the infection rate of hospital-acquired COVID-19 among ESFHCWs. HCWs with less than 10 years of work seniority, anxiety disorder, and other medications (gastrointestinal, anti-inflammatory and anti-asthmatic, and hypnotic sedative drugs) were probably not suitable for participating in emergency assistant actions because of their poor SIPC scores. Further studies are needed to develop the selection criteria for ESFHCWs.

Volatility of the COVID-19 vaccine hesitancy: sentiment analysis conducted in Brazil

Background

Vaccine hesitancy is a phenomenon that can interfere with the expansion of vaccination coverage and is positioned as one of the top 10 global health threats. Previous studies have explored factors that affect vaccine hesitancy, how it behaves in different locations, and the profile of individuals in which it is most present. However, few studies have analyzed the volatility of vaccine hesitancy.

Objective

Identify the volatility of vaccine hesitancy manifested in social media.

Methods

Twitter's academic application programming interface was used to retrieve all tweets in Brazilian Portuguese mentioning the COVID-19 vaccine in 3 months (October 2020, June 2021, and October 2021), retrieving 1,048,576 tweets. A sentiment analysis was performed using the Orange software with the lexicon Multilingual sentiment in Portuguese.

Results

The feelings associated with vaccine hesitancy were volatile within 1 month, as well as throughout the vaccination process, being positioned as a resilient phenomenon. The themes that nurture vaccine hesitancy change dynamically and swiftly and are often associated with other topics that are also affecting society.

Conclusion

People that manifest the vaccine hesitancy present arguments that vary in a short period of time, what demand that government strategies to mitigate vaccine hesitancy effects be agile and counteract the expressed fear, by presenting scientific arguments.

COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets

Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy-sadness, trust-disgust, fear-anger, surprise-anticipation, and negative-positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.

COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India

This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.

Vaccine hesitancy and post-vaccination adherence to safety measures: A mixed-method study

Background

Despite being recognized as one of the most successful public health measures, vaccination is still considered to be unnecessary and unreliable in the context of the COVID-19 pandemic. The current study utilized a two-pronged approach in analyzing vaccine hesitancy and health behaviors after vaccination by employing a mixed-method design. Phase 1 was aimed at identifying predictors of COVID-19 vaccine hesitancy and acceptance among the Pakistani population using protection motivation theory (PMT), whereas Phase 2 was aimed at exploring the factors related to the vaccination of COVID-19.

Method

A convenient sample of 1,736 individuals from the vaccine-eligible population (12 years and above) was selected to collect data on vaccine hesitancy and acceptance (Phase 1). Phase 2 of the study explored post-vaccination health behaviors, especially adherence to safety measures for COVID-19, through 23 in-depth interviews with the vaccinated population.

Results

Multiple regression analyses showed that response cost is a major predictor of vaccine hesitancy (in Phase 1). In terms of the role of demographic variables, the results showed that being male (for severity: B = −0.481; threat appraisal: B = −0.737), old age (B = −0.044), not vaccinated, and not infected with COVID-19 (themselves and family members) are strongly associated with vaccination hesitancy. Results of thematic analysis in Phase 2 revealed that perceived individual experience and insensitivity toward the severity of the disease are strongly associated with a lack of adherence to safety measures of COVID-19. Faith and religious beliefs and reliance on traditional remedies are also key predictors of people's general non-compliance to health behaviors. One interesting aspect that was revealed in the analysis was the general financially and socially destabilized situation in the context of developing countries that contributed to general apathy in the pandemic situation.

Conclusion

The findings of the current study may help in devising a health model for the public from the developing world to deal with future pandemic situations.

Perception of COVID-19 vaccination among Indian Twitter users: computational approach

Vaccination has been a hot topic in the present COVID-19 context. The government, public health stakeholders and media are all concerned about how to get the people vaccinated. The study was intended to explore the perception and emotions of the Indians citizens toward COVID-19 vaccine from Twitter messages. The tweets were collected for the period of 6 months, from mid-January to June, 2021 using hash-tags and keywords specific to India. Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. Theme, sentiment and emotion wise engagement and reachability metrics were assessed. Hash-tag frequency of COVID-19 vaccine brands were also identified and evaluated. Information regarding 'Co-WIN app and availability of vaccine' was widely discussed and also received highest engagement and reachability among Twitter users. Among the various emotions, trust was expressed the most, which highlights the acceptance of vaccines among the Indian citizens. The hash-tags frequency of vaccine brands shows that Covishield was popular in the month of March 2021, and Covaxin in April 2021. The results from the study will help stakeholders to efficiently use social media to disseminate COVID-19 vaccine information on popular concerns. This in turn will encourage citizens to be vaccinated and achieve herd immunity. Similar methodology can be adopted in future to understand the perceptions and concerns of people in emergency situations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42001-023-00203-0.

A novel sentiment analysis method based on multi-scale deep learning

As the college students have been a most active user group in various social media, it remains significant to make effective sentiment analysis for college public opinions. Capturing the direction of public opinion in the student community in a timely manner and guiding students to develop the right values can help in the ideological management of universities. Universally, the recurrent neural networks have been the mainstream technology in terms of sentiment analysis. Nevertheless, the existing research works more emphasized semantic characteristics in vertical direction, yet failing to capture sematic characteristics in horizonal direction. In other words, it is supposed to increase more balance into sentiment analysis models. To remedy such gap, this paper presents a novel sentiment analysis method based on multi-scale deep learning for college public opinions. To fit for bidirectional semantic characteristics, a typical sequential neural network with two propagation paths is selected as the backbone. It is then extended with more layers in horizonal direction. Such design is able to balance both model depth and model breadth. At last, some experiments on a real-world social media dataset are conducted for evaluation, well acknowledging efficiency of the proposed analysis model.

Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19.

Generalizing factors of COVID-19 vaccine attitudes in different regions: A summary generation and topic modeling approach

Objective

The goal of this study is to use summary generation and topic modeling to identify factors contributing to vaccine attitudes for three different vaccine brands, with the aim of generalizing these factors across different regions.

Methods

A total of 5562 tweets about three vaccine brands (Sinovac, AstraZeneca, and Pfizer) were collected from 14 December 2020 to 30 December 2021. BERTopic clustering is used to group the tweets into topics, and then contrastive learning (CL) is adopted to generate summaries of each topic. The main content of each topic is generalized into three factors that contribute to vaccine attitudes: vaccine-related factors, health system-related factors, and individual social attributes.

Results

BERTopic clustering outperforms Latent Dirichlet Allocation clustering in our analysis. It can also be found that using CL for summary generation helped to better model the topics, particularly at the center-point of the clustering. Our model identifies three main factors contributing to vaccine attitudes that are consistent across different regions.

Conclusions

Our study demonstrates the effectiveness of deep learning methods for identifying factors contributing to vaccine attitudes in different regions. By determining these factors, policymakers and medical institutions can develop more effective strategies for addressing concerns related to the vaccination process.

Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.

Improving Public Health Policy by Comparing the Public Response during the Start of COVID-19 and Monkeypox on Twitter in Germany: A Mixed Methods Study

Little is known about monkeypox public concerns since its widespread emergence in many countries. Tweets in Germany were examined in the first three months of COVID-19 and monkeypox to examine concerns and issues raised by the public. Understanding views and positions of the public could help to shape future public health campaigns. Few qualitative studies reviewed large datasets, and the results provide the first instance of the public thinking comparing COVID-19 and monkeypox. We retrieved 15,936 tweets from Germany using query words related to both epidemics in the first three months of each one. A sequential explanatory mixed methods research joined a machine learning approach with thematic analysis using a novel rapid tweet analysis protocol. In COVID-19 tweets, there was the selfing construct or feeling part of the emerging narrative of the spread and response. In contrast, during monkeypox, the public considered othering after the fatigue of the COVID-19 response, or an impersonal feeling toward the disease. During monkeypox, coherence and reconceptualization of new and competing information produced a customer rather than a consumer/producer model. Public healthcare policy should reconsider a one-size-fits-all model during information campaigns and produce a strategic approach embedded within a customer model to educate the public about preventative measures and updates. A multidisciplinary approach could prevent and minimize mis/disinformation.