Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines

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.

Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance

The primary goal of this study is to examine the association between vaccine rhetoric on Twitter and the public's uptake rates of COVID-19 vaccines in the United States, compared to the extent of an association between self-reported vaccine acceptance and the CDC's uptake rates. We downloaded vaccine-related posts on Twitter in real-time daily for 13 months, from October 2021 to September 2022, collecting over half a billion tweets. A previously validated deep-learning algorithm was then applied to (1) filter out irrelevant tweets and (2) group the remaining relevant tweets into pro-, anti-, and neutral vaccine sentiments. Our results indicate that the tweet counts (combining all three sentiments) were significantly correlated with the uptake rates of all stages of COVID-19 shots (p < 0.01). The self-reported level of vaccine acceptance was not correlated with any of the stages of COVID-19 shots (p > 0.05) but with the daily new infection counts. These results suggest that although social media posts on vaccines may not represent the public's opinions, they are aligned with the public's behaviors of accepting vaccines, which is an essential step for developing interventions to increase the uptake rates. In contrast, self-reported vaccine acceptance represents the public's opinions, but these were not correlated with the behaviors of accepting vaccines. These outcomes provide empirical support for the validity of social media analytics for gauging the public's vaccination behaviors and understanding a nuanced perspective of the public's vaccine sentiment for health emergencies.

Comparison of reporting rates of arthritis and arthralgia following AstraZeneca, Pfizer-BioNTech, Moderna, and Janssen vaccine administration against SARS-CoV-2 in 2021: analysis of European pharmacovigilance large-scale data

This study aimed to investigate the reporting rates of arthritis and arthralgia following the administration of four vaccines against SARS-CoV-2: Pfizer-BioNTech (Tozinameran), Moderna (CX-024414), AstraZeneca (Chadox1 NCOV-19), and Janssen (AD26.COV2.S) in 2021. We used data from the EudraVigilance database, specifically analyzing spontaneous reports of suspected adverse reactions (ADRs) from the European Union (EU)/European Economic Area (EEA) region. Age-group-specific reporting rates were calculated by dividing the number of arthralgia and arthritis reports per 1,000,000 vaccine doses administered per age group. Reporting rates were compared using a rate ratio among the four vaccines, using the AstraZeneca vaccine as a comparator. The AstraZeneca vaccine was associated with the highest rate of arthralgia across all age groups. Arthritis reporting rates were significantly lower, with the AstraZeneca vaccine having the highest rates in most age groups, except the 60-69 and 80+ groups, where the Janssen and Pfizer-BioNTech vaccines demonstrated higher reporting rates, respectively. The distribution of arthritis rates did not follow the arthralgia pattern, being higher in the 50-79 age group. This study is the first spontaneous reporting system analysis of arthritis reporting rates post-SARS-CoV-2 vaccination at a European level, revealing a higher reporting of suspected musculoskeletal adverse reactions after AstraZeneca vaccination. The findings underscore the need to consider commonly reported events like arthralgia in risk-benefit assessments prior to vaccination against SARS-CoV-2. Given the high prevalence of rheumatic and musculoskeletal diseases and vaccine hesitancy in this population, our results could influence vaccine choice and acceptance.

The role of social media in public health crises caused by infectious disease: a scoping review

IMPORTANCE

The onset of the COVID-19 global pandemic highlighted the increasing role played by social media in the generation, dissemination and consumption of outbreak-related information.

OBJECTIVE

The objective of the current review is to identify and summarise the role of social media in public health crises caused by infectious disease, using a five-step scoping review protocol.

EVIDENCE REVIEW

Keyword lists for two categories were generated: social media and public health crisis. By combining these keywords, an advanced search of various relevant databases was performed to identify all articles of interest from 2000 to 2021, with an initial retrieval date of 13 December 2021. A total of six medical and health science, psychology, social science and communication databases were searched: PubMed, Web of Science, Scopus, Embase, PsycINFO and CNKI. A three-stage screening process against inclusion and exclusion criteria was conducted.

FINDINGS

A total of 338 studies were identified for data extraction, with the earliest study published in 2010. Thematic analysis of the role of social media revealed three broad themes: surveillance monitoring, risk communication and disease control. Within these themes, 12 subthemes were also identified. Within surveillance monitoring, the subthemes were disease detection and prediction, public attitude and attention, public sentiment and mental health. Within risk communication, the subthemes were health advice, information-seeking behaviour, infodemics/misinformation circulation, seeking help online, online distance education and telehealth. Finally, within disease control, the subthemes were government response, public behaviour change and health education information quality. It was clear that the pace of research in this area has gradually increased over time as social media has evolved, with an explosion in attention following the outbreak of COVID-19.

CONCLUSIONS AND RELEVANCE

Social media has become a hugely powerful force in public health and cannot be ignored or viewed as a minor consideration when developing public health policy. Limitations of the study are discussed, along with implications for government, health authorities and individual users. The pressing need for government and health authorities to formalise evidence-based strategies for communicating via social media is highlighted, as well as issues for individual users in assessing the quality and reliability of information consumed on social media platforms.

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.

Leveraging Multiyear, Geospatial Social Media Data for Health Policy Evaluations: Lessons From the Philadelphia Beverage Tax

CONTEXT

Public reactions to health policies are vital to understand policy sustainability and impact but have been elusively difficult to dynamically measure. The 2021 launch of the Twitter Academic Application Programming Interface (API), allowing for historical tweet analyses, represents a potentially powerful tool for complex, comprehensive policy analyses.

OBJECTIVE

Using the Philadelphia Beverage Tax (implemented January 2017) as a case study, this research extracted longitudinal and geographic changes in sentiments, and key influencers in policy-related conversations.

DESIGN

The Twitter API was used to retrieve all publicly available tweets related to the Tax between 2016 and 2019.

SETTING

Twitter.

PARTICIPANTS

Users who posted publicly available tweets related to the Philadelphia Beverage Tax (PBT).

MAIN OUTCOME

Tweet content, frequency, sentiment, and user-related information.

MEASURES

Tweet content, authors, engagement, and location were analyzed in parallel to key PBT events. Published emotional lexicons were used for sentiment analyses.

RESULTS

A total of 45 891 tweets were retrieved (1311 with geolocation data). Changes in the tweet volume and sentiment were strongly driven by Tax-related litigation. While anger and fear increased in the months prior to the policy's implementation, they progressively decreased after its implementation; trust displayed an inverse trend. The 50 tweeters with the highest positive engagement included media outlets (n = 24), displaying particularly high tweet volume/engagement, and public personalities (n = 10), displaying the greatest polarization in tweet sentiment. Most geo-located tweets, reflecting 321 unique locations, were from the Philadelphia region (55.2%). Sentiment and positive engagement varied, although concentrations of negative sentiments were observed in some Philadelphia suburbs.

CONCLUSIONS

Findings highlighted how longitudinal Twitter data can be leveraged to deconstruct specific, dynamic insights on public policy reactions and information dissemination to inform better policy implementation and evaluation (eg, anticipating catalysts for both heightened public interest and geographic, sentiment changes in policy conversations). This study provides policymakers a blueprint to conduct similar cost and time efficient yet dynamic and multifaceted health policy evaluations.

ChatGPT in education: global reactions to AI innovations

The release and rapid diffusion of ChatGPT have caught the attention of educators worldwide. Some educators are enthusiastic about its potential to support learning. Others are concerned about how it might circumvent learning opportunities or contribute to misinformation. To better understand reactions about ChatGPT concerning education, we analyzed Twitter data (16,830,997 tweets from 5,541,457 users). Based on topic modeling and sentiment analysis, we provide an overview of global perceptions and reactions to ChatGPT regarding education. ChatGPT triggered a massive response on Twitter, with education being the most tweeted content topic. Topics ranged from specific (e.g., cheating) to broad (e.g., opportunities), which were discussed with mixed sentiment. We traced that authority decisions may influence public opinions. We discussed that the average reaction on Twitter (e.g., using ChatGPT to cheat in exams) differs from discussions in which education and teaching-learning researchers are likely to be more interested (e.g., ChatGPT as an intelligent learning partner). This study provides insights into people's reactions when new groundbreaking technology is released and implications for scientific and policy communication in rapidly changing circumstances.

Influence of Tweets Indicating False Rumors on COVID-19 Vaccination: Case Study

BACKGROUND

As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people's lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity. However, in places such as Japan, where vaccination is voluntary, there are people who choose not to receive the vaccine, even if an effective vaccine is offered. To promote vaccination, it is necessary to clarify what kind of information on social media can influence attitudes toward vaccines.

OBJECTIVE

False rumors and counterrumors are often posted and spread in large numbers on social media, especially during emergencies. In this paper, we regard tweets that contain questions or point out errors in information as counterrumors. We analyze counterrumors tweets related to the COVID-19 vaccine on Twitter. We aimed to answer the following questions: (1) what kinds of COVID-19 vaccine-related counterrumors were posted on Twitter, and (2) are the posted counterrumors related to social conditions such as vaccination status?

METHODS

We use the following data sets: (1) counterrumors automatically collected by the "rumor cloud" (18,593 tweets); and (2) the number of COVID-19 vaccine inoculators from September 27, 2021, to August 15, 2022, published on the Prime Minister's Office's website. First, we classified the contents contained in counterrumors. Second, we counted the number of COVID-19 vaccine-related counterrumors from data set 1. Then, we examined the cross-correlation coefficients between the numbers of data sets 1 and 2. Through this verification, we examined the correlation coefficients for the following three periods: (1) the same period of data; (2) the case where the occurrence of the suggestion of counterrumors precedes the vaccination (negative time lag); and (3) the case where the vaccination precedes the occurrence of counterrumors (positive time lag). The data period used for the validation was from October 4, 2021, to April 18, 2022.

RESULTS

Our classification results showed that most counterrumors about the COVID-19 vaccine were negative. Moreover, the correlation coefficients between the number of counterrumors and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at -8, -7, and -1 weeks of lag. Results suggest that the number of vaccine inoculators tended to increase with an increase in the number of counterrumors. Significant correlation coefficients of 0.5 to 0.6 were observed for lags of 1 week or more and 2 weeks or more. This implies that an increase in vaccine inoculators increases the number of counterrumors. These results suggest that the increase in the number of counterrumors may have been a factor in inducing vaccination behavior.

CONCLUSIONS

Using quantitative data, we were able to reveal how counterrumors influence the vaccination status of the COVID-19 vaccine. We think that our findings would be a foundation for considering countermeasures of vaccination.

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

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

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.

Crisis Communication during COVID-19: English, French, Portuguese, and Spanish Discourse of AstraZeneca Vaccine and Omicron Variant on Social Media

Social media have been the arena of different types of discourse during the COVID-19 pandemic. We aim to characterize public discourse during health crises in different international communities. Using Tweetpy and keywords related to the research, we collected 3,748,302 posts from the English, French, Portuguese, and Spanish Twitter communities related to two crises during the pandemic: (a) the AstraZeneca COVID-19 vaccine, and (b) the Omicron variant. In relation to AstraZeneca, 'blood clot' was the main focus of public discourse. Using quantitative classifications and natural language processing algorithms, results are obtained for each language. The English and French discourse focused more on "death", and the most negative sentiment was generated by the French community. The Portuguese discourse was the only one to make a direct reference to a politician, the former Brazilian President Bolsonaro. In the Omicron crisis, the public discourse mainly focused on infection cases follow-up and the number of deaths, showing a closer public discourse to the actual risk. The public discourse during health crises might lead to different behaviours. While public discourse on AstraZeneca might contribute as a barrier for preventive measures by increasing vaccine hesitancy, the Omicron discourse could lead to more preventive behaviours by the public, such as the use of masks. This paper broadens the scope of crisis communication by revealing social media's role in the constructs of public discourse.

[Sentiments in the COVID-19 crisis communication of German authorities and independent experts on Twitter : A sentiment analysis for the first year of the pandemic]

BACKGROUND

At the beginning of the COVID‑19 pandemic in Germany, there was great uncertainty among the population and among those responsible for crisis communication. A substantial part of the communication from experts and the responsible authorities took place on social media, especially on Twitter. The positive, negative, and neutral sentiments (emotions) conveyed there during crisis communication have not yet been comparatively studied for Germany.

STUDY AIM

Sentiments in Twitter messages from various (health) authorities and independent experts on COVID‑19 will be evaluated for the first pandemic year (1 January 2020 to 15 January 2021) to provide a knowledge base for improving future crisis communication.

MATERIAL AND METHODS

From n = 39 Twitter actors (21 authorities and 18 experts), n = 8251 tweets were included in the analysis. The sentiment analysis was done using the so-called lexicon approach, a method within the social media analytics framework to detect sentiments. Descriptive statistics were calculated to determine, among other things, the average polarity of sentiments and the frequencies of positive and negative words in the three phases of the pandemic.

RESULTS AND DISCUSSION

The development of emotionality in COVID‑19 tweets and the number of new infections in Germany run roughly parallel. The analysis shows that the polarity of sentiments is negative on average for both groups of actors. Experts tweet significantly more negatively about COVID‑19 than authorities during the study period. Authorities communicate close to the neutrality line in the second phase, that is, neither distinctly positive nor negative.

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

In 2019 there was an outbreak of coronavirus pandemic also known as COVID-19. Many scientists believe that the pandemic originated from Wuhan, China, before spreading to other parts of the globe. To reduce the spread of the disease, decision makers encouraged measures such as hand washing, face masking, and social distancing. In early 2021, some countries including the United States began administering COVID-19 vaccines. Vaccination brought a relief to the public; it also generated a lot of debates from anti-vaccine and pro-vaccine groups. The controversy and debate surrounding COVID-19 vaccine influenced the decision of several people in either to accept or reject vaccination. Because of data limitations, social media data, collected through live streaming public tweets using an Application Programming Interface (API) search, is considered a viable and reliable resource to study the opinion of the public on Covid-19 vaccine hesitancy. Thus, this study examines 3 sentiment computation methods (Azure Machine Learning, VADER, and TextBlob) to analyze COVID-19 vaccine hesitancy. Five learning algorithms (Random Forest, Logistics Regression, Decision Tree, LinearSVC, and Naïve Bayes) with different combination of three vectorization methods (Doc2Vec, CountVectorizer, and TF-IDF) were deployed. Vocabulary normalization was threefold; potter stemming, lemmatization, and potter stemming with lemmatization. For each vocabulary normalization strategy, we designed, developed, and evaluated 42 models. The study shows that Covid-19 vaccine hesitancy slowly decreases over time; suggesting that the public gradually feels warm and optimistic about COVID-19 vaccination. Moreover, combining potter stemming and lemmatization increased model performances. Finally, the result of our experiment shows that TextBlob + TF-IDF + LinearSVC has the best performance in classifying public sentiment into positive, neutral, or negative with an accuracy, precision, recall and F1 score of 0.96752, 0.96921, 0.92807 and 0.94702 respectively. It means that the best performance was achieved when using TextBlob sentiment score, with TF-IDF vectorization and LinearSVC classification model. We also found out that combining two vectorizations (CountVectorizer and TF-IDF) decreases model accuracy.

Mining the vaccination willingness of China using social media data

OBJECTIVE

Vaccination is one of the most powerful and effective protective measures against Coronavirus disease 2019 (COVID-19). Currently, several blogs hold content on vaccination attitudes expressed on social media platforms, especially Sina Weibo, which is one of the largest social media platforms in China. Therefore, Weibo is a good data source for investigating public opinions about vaccination attitudes. In this paper, we aimed to effectively mine blogs to quantify the willingness of the public to get the COVID-19 vaccine.

MATERIALS AND METHODS

First, data including 144,379 Chinese blogs from Weibo, were collected between March 24 and April 28, 2021. The data were cleaned and preprocessed to ensure the quality of the experimental data, thereby reducing it to an experimental dataset of 72,496 blogs. Second, we employed a new fusion sentiment analysis model to analyze the sentiments of each blog. Third, the public's willingness to get the COVID-19 vaccine was quantified using the organic fusion of sentiment distribution and information dissemination effect.

RESULTS

(1) The intensity of bloggers' sentiment toward COVID-19 vaccines changed over time. (2) The extremum of positive and negative sentiment intensities occurred when hot topics related to vaccines appeared. (3) The study revealed that the public's willingness to get the COVID-19 vaccine and the actual vaccination doses shares a linear relationship.

CONCLUSION

We proposed a method for quantifying the public's vaccination willingness from social media data. The effectiveness of the method was demonstrated by a significant consistency between the estimates of public vaccination willingness and actual COVID-19 vaccination doses.

On the development of an information system for monitoring user opinion and its role for the public

Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users' opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem's social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system's study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people's moods and attitudes to the Government's policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., "neutral" sentiment was present in most of the Tweets. It was followed by "negative" and "positive" sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

Stroke and TIA Survivors' Perceptions of the COVID-19 Vaccine and Influences on Its Uptake: Cross Sectional Survey

BACKGROUND

People who have experienced a stroke or transient ischaemic attack (TIA) have greater risks of complications from COVID-19. Therefore, vaccine uptake in this vulnerable population is important. To prevent vaccine hesitancy and maximise compliance, we need to better understand individuals' views on the vaccine.

OBJECTIVES

We aimed to explore perspectives of the COVID-19 vaccine and influences on its uptake from people who have experienced a stroke or TIA.

METHOD

A cross-sectional, electronic survey comprising multiple choice and free text questions. Convenience sampling was used to recruit people who have experienced a stroke/TIA in the UK/Ireland.

RESULTS

The survey was completed by 377 stroke/TIA survivors. 87% (328/377) had either received the first vaccine dose or were booked to have it. The vaccine was declined by 2% (7/377) and 3% (11/377) had been offered the vaccine but not yet taken it up. 8% (30/377) had not been offered the vaccine despite being eligible. Some people expressed concerns around the safety of the vaccine (particularly risk of blood clots and stroke) and some were hesitant to have the second vaccine. Societal and personal benefits were motivations for vaccine uptake. There was uncertainty and lack of information about risk of COVID-19 related complications specifically for people who have experienced a stroke or TIA.

CONCLUSION

Despite high uptake of the first vaccine, some people with stroke and TIA have legitimate concerns and information needs that should be addressed. Our findings can be used to identify targets for behaviour change to improve vaccine uptake specific to stroke/TIA patients.

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis

OBJECTIVES

The present study aims to examine coronavirus disease 2019 (COVID-19) vaccination discussions on Twitter in Turkey and conduct sentiment analysis.

METHODS

The current study performed sentiment analysis of Twitter data with the artificial intelligence (AI) Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first COVID-19 case was seen in Turkey, to April 18, 2022. A total of 10,308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing ML (ML) classifiers.

RESULTS

It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGBoost (XGB) algorithm had higher scores.

CONCLUSIONS

Three of 4 tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.

Seasonality of Hashimoto Thyroiditis: Infodemiology Study of Google Trends Data

BACKGROUND

Hashimoto thyroiditis (HT) is an autoimmune thyroid disease and the leading cause of hypothyroidism in areas with sufficient iodine intake. The quality-of-life impact and financial burden of hypothyroidism and HT highlight the need for additional research investigating the disease etiology with the aim of revealing potential modifiable risk factors.

OBJECTIVE

Implementation of measures against such risk factors, once identified, has the potential to lessen the financial burden while also improving the quality of life of many individuals. Therefore, we aimed to examine the potential seasonality of HT in Europe using the Google Trends data to explore whether there is a seasonal characteristic of Google searches regarding HT, examine the potential impact of the countries' geographic location on the potential seasonality, and identify potential modifiable risk factors for HT, thereby inspiring future research on the topic.

METHODS

Monthly Google Trends data on the search topic "Hashimoto thyroiditis" were retrieved in a 17-year time frame from January 2004 to December 2020 for 36 European countries. A cosinor model analysis was conducted to evaluate potential seasonality. Simple linear regression was used to estimate the potential effect of latitude and longitude on seasonal amplitude and phase of the model outputs.

RESULTS

Of 36 included European countries, significant seasonality was observed in 30 (83%) countries. Most phase peaks occurred in spring (14/30, 46.7%) and winter (8/30, 26.7%). A statistically significant effect was observed regarding the effect of geographical latitude on cosinor model amplitude (y = -3.23 + 0.13 x; R2=0.29; P=.002). Seasonal increases in HT search volume may therefore be a consequence of an increased incidence or higher disease activity. It is particularly interesting that in most countries, a seasonal peak occurred in spring and winter months; when viewed in the context of the statistically significant impact of geographical latitude on seasonality amplitude, this may indicate the potential role of vitamin D levels in the seasonality of HT.

CONCLUSIONS

Significant seasonality of HT Google Trends search volume was observed in our study, with seasonal peaks in most countries occurring in spring and winter and with a significant impact of latitude on seasonality amplitude. Further studies on the topic of seasonality in HT and factors impacting it are required.

Negative COVID-19 Vaccine Information on Twitter: Content Analysis

Background

Social media platforms, such as Facebook, Instagram, Twitter, and YouTube, have a role in spreading anti-vaccine opinion and misinformation. Vaccines have been an important component of managing the COVID-19 pandemic, so content that discourages vaccination is generally seen as a concern to public health. However, not all negative information about vaccines is explicitly anti-vaccine, and some of it may be an important part of open communication between public health experts and the community.

Objective

This research aimed to determine the frequency of negative COVID-19 vaccine information on Twitter in the first 4 months of 2021.

Methods

We manually coded 7306 tweets sampled from a large sampling frame of tweets related to COVID-19 and vaccination collected in early 2021. We also coded the geographic location and mentions of specific vaccine producers. We compared the prevalence of anti-vaccine and negative vaccine information over time by author type, geography (United States, United Kingdom, and Canada), and vaccine developer.

Results

We found that 1.8% (131/7306) of tweets were anti-vaccine, but 21% (1533/7306) contained negative vaccine information. The media and government were common sources of negative vaccine information but not anti-vaccine content. Twitter users from the United States generated the plurality of negative vaccine information; however, Twitter users in the United Kingdom were more likely to generate negative vaccine information. Negative vaccine information related to the Oxford/AstraZeneca vaccine was the most common, particularly in March and April 2021.

Conclusions

Overall, the volume of explicit anti-vaccine content on Twitter was small, but negative vaccine information was relatively common and authored by a breadth of Twitter users (including government, medical, and media sources). Negative vaccine information should be distinguished from anti-vaccine content, and its presence on social media could be promoted as evidence of an effective communication system that is honest about the potential negative effects of vaccines while promoting the overall health benefits. However, this content could still contribute to vaccine hesitancy if it is not properly contextualized.

Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts

Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community-based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

Media Data and Vaccine Hesitancy: Scoping Review

Background

Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.

Objective

This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health.

Methods

This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes.

Results

In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals-in particular cases, deaths, and scandals-which suggests a more volatile period for the spread of information.

Conclusions

The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement-not supplant-current practices in public health research.

Analyzing Iranian opinions toward COVID-19 vaccination

OBJECTIVES

The aim of this study was to assess Iranian tweets in order to: (1) analyze Iranian views toward COVID-19-vaccination; (2) compare Iranian views toward homegrown and imported COVID-19-vaccines; (3) present an effective model for sentiment analysis tasks regarding critical issues such as COVID-19-vaccination.

DESIGN AND METHODS

Persian tweets mentioning homegrown and imported vaccines were retrieved between April 1 and and September 30, 2021. The sentiments of retrieved tweets were identified using a deep-learning sentiment-analysis model. A sarcasm detection model, based on a random forest classifier, was used to identify sarcastic tweets and thus minimize misclassification. Finally, Iranian views toward COVID-19 vaccination were investigated.

RESULTS

Subtle differences were found in the number of positive sentiments toward homegrown and imported vaccines, with the latter having dominant positive polarity. Negative sentiments regarding homegrown and imported vaccines increased in some months. No significant differences were observed between the percentages of overall positive and negative opinions toward vaccination.

CONCLUSION

It is worrisome that negative sentiments toward homegrown and imported vaccines increased in some months in Iran. Health organizations can focus on Twitter in order to promote positive messaging toward COVID-19 vaccination. Sarcasm detection enabled the identification of tweets that ironically stated positive sentiments toward vaccination, thus improving the accuracy of the sentiment analysis results. Our sentiment analysis-sarcasm detection model is a reliable tool for mitigating classification problems.

Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia

Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of “az”, “pfizer”, “sinovac”, and “mix” for determinants’ assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and “pfizer” and “mix” were the strongest determinants of the tweet’s sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media.

Monitoring Mentions of COVID-19 Vaccine Side Effects from Japanese and Indonesian Twitter: Infodemiological Study (Preprint)

Background

The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy.

Objective

This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia.

Methods

We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country.

Results

We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets.

Conclusions

We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed.

COVID-19 vaccine sentiment analysis using public opinions on Twitter

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.

Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter

Background

On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people’s perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition.

Objective

The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk’s disclosure.

Methods

We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms “Aspergers” or “Aspie.” The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool.

Results

We extracted a total of 227 popular tweets (34 posted the week before Musk’s announcement and 193 posted the week after). We classified 210 (92.5%) of the tweets as neutral, 13 (5.7%) tweets as informative, and 4 (1.8%) as containing misinformation. Both informative and misinformative tweets were posted after Musk’s disclosure. Popular tweets posted before Musk’s disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement.

Conclusions

The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition.

Gulf Countries’ Citizens’ Acceptance of COVID-19 Vaccines—A Machine Learning Approach

The COVID-19 pandemic created a global emergency in many sectors. The spread of the disease can be subdued through timely vaccination. The COVID-19 vaccination process in various countries is ongoing and is slowing down due to multiple factors. Many studies on European countries and the USA have been conducted and have highlighted the public’s concern that over-vaccination results in slowing the vaccination rate. Similarly, we analyzed a collection of data from the gulf countries’ citizens’ COVID-19 vaccine-related discourse shared on social media websites, mainly via Twitter. The people’s feedback regarding different types of vaccines needs to be considered to increase the vaccination process. In this paper, the concerns of Gulf countries’ people are highlighted to lessen the vaccine hesitancy. The proposed approach emphasizes the Gulf region-specific concerns related to COVID-19 vaccination accurately using machine learning (ML)-based methods. The collected data were filtered and tokenized to analyze the sentiments extracted using three different methods: Ratio, TextBlob, and VADER methods. The sentiment-scored data were classified into positive and negative tweeted data using a proposed LSTM method. Subsequently, to obtain more confidence in classification, the in-depth features from the proposed LSTM were extracted and given to four different ML classifiers. The ratio, TextBlob, and VADER sentiment scores were separately provided to LSTM and four machine learning classifiers. The VADER sentiment scores had the best classification results using fine-KNN and Ensemble boost with 94.01% classification accuracy. Given the improved accuracy, the proposed scheme is robust and confident in classifying and determining sentiments in Twitter discourse.

Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis

Background

COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics.

Objective

This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination.

Methods

We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan.

Results

The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative.

Conclusions

Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence.

Harnessing Twitter data to survey public attention and attitudes towards COVID-19 vaccines in the UK

Attitudes to COVID-19 vaccination vary considerably within and between countries. Although the contribution of socio-demographic factors to these attitudes has been studied, the role of social media and how it interacts with news about vaccine development and efficacy is uncertain. We examined around 2 million tweets from 522,893 persons in the UK from November 2020 to January 2021 to evaluate links between Twitter content about vaccines and major scientific news announcements about vaccines. The proportion of tweets with negative vaccine content varied, with reductions of 20-24% on the same day as major news announcement. However, the proportion of negative tweets reverted back to an average of around 40% within a few days. Engagement rates were higher for negative tweets. Public health messaging could consider the dynamics of Twitter-related traffic and the potential contribution of more targeted social media campaigns to address vaccine hesitancy.