Positive attitudes towards COVID-19 vaccines: A cross-country analysis

Cross-sectional study on intention to be vaccinated against Coronavirus Disease 2019 (COVID-19) in Benin and Senegal: A structural equation modeling (SEM)

Vaccination is considered one of the solutions to the Coronavirus Disease 2019 (COVID-19) pandemic. However, a small proportion of the population were fully vaccinated in Benin (20.9%) and Senegal (7.6%) by December 2022. This study explores the determinants of intent to vaccinate. This was a cross-sectional, descriptive, and analytical study of 865 Beninese and 813 Senegalese aged 18 years and older. Marginal quota sampling by age, gender and region was adopted. Data collection, using a survey instrument based on the Random Digit Dialing method, was conducted from December 24, 2020, to January 16, 2021, in Senegal and from March 29 to May 14, 2021, in Benin. The questionnaire used the Theory of Planned Behavior and the Health Belief Model. The influence of factors was assessed using a structural equation model based on a diagonally weighted least squares estimator to account for ordered categorical data (Likert scales). In Benin and Senegal, the intention to vaccinate against COVID-19 is influenced by distinct factors. In Benin, social influence (β = 0.42, p = 0.003) and perception of vaccine safety (β = -0.53, p<0.001) play pivotal roles, suggesting those socially influenced have a higher vaccination intention. In Senegal, vaccination intentions are primarily driven by positive attitudes towards the vaccine (β = 0.65, p = 0.013) and social influence (β = 0.25, p = 0.048). This underscores the importance of individual beliefs, personal perceptions, and supportive social contexts in decision-making. Notably, positive vaccination attitudes and perceptions in both countries are strongly tied to increased social influence. While nuances exist between Benin and Senegal regarding factors influencing COVID-19 vaccination intentions, both nations underscore the pivotal roles of social influence and individual vaccination perceptions. Emphasizing trust in vaccine safety and promoting positive attitudes through effective communication are crucial for enhancing vaccination uptake in these West African countries.

Using the Theory of Planned Behavior to determine COVID-19 vaccination intentions and behavior among international and domestic college students in the United States

Vaccination is the most effective strategy for preventing infectious diseases such as COVID-19. College students are important targets for COVID-19 vaccines given this population's lower intentions to be vaccinated; however, limited research has focused on international college students' vaccination status. This study explored how psychosocial factors from the Theory of Planned Behavior (TPB; attitudes, perceived behavioral control, subjective norms, and behavioral intentions) related to students' receipt of the full course of COVID-19 vaccines and their plans to receive a booster. Students were recruited via Amazon mTurk and the Office of the Registrar at a U.S. state university. We used binary logistic regression to examine associations between students' psychosocial factors and full COVID-19 vaccination status. Hierarchical multiple regression was employed to evaluate relationships between these factors and students' intentions to receive a booster. The majority of students in our sample (81% of international students and 55% of domestic students) received the complete vaccination series. Attitudes were significantly associated with all students' full vaccination status, while perceived behavioral control was significantly associated with domestic students' status. Students' intentions to receive COVID-19 vaccines were significantly correlated with their intentions to receive a booster, with international students scoring higher on booster intentions. Among the combined college student population, attitudes, intentions to receive COVID-19 vaccines, and subjective norms were significantly related to students' intentions to receive a booster. Findings support the TPB's potential utility in evidence-based interventions to enhance college students' COVID-19 vaccination rates. Implications for stakeholders and future research directions are discussed.

Reactions to macro-level shocks and re-examination of adaptation theory using Big Data

Since 2020, the world has faced two unprecedented shocks: lockdowns (regulation) and the invasion of Ukraine (war). Although we realise the health and economic effects of these shocks, more research is needed on the effect on happiness and whether the type of shock plays a role. Therefore, in this paper, we determine whether these macro-level shocks affected happiness, how these effects differ, and how long it takes for happiness to adapt to previous levels. The latter will allow us to test whether adaptation theory holds at the macro level. We use a unique dataset of ten countries spanning the Northern and Southern hemispheres derived from tweets extracted in real-time per country. Applying Natural Language Processing, we obtain these tweets' underlying sentiment scores, after which we calculate a happiness score (Gross National Happiness) and derive daily time series data. Our Twitter dataset is combined with Oxford's COVID-19 Government Response Tracker data. Considering the results of the Difference-in-Differences and event studies jointly, we are confident that the shocks led to lower happiness levels, both with the lockdown and the invasion shock. We find that the effect size is significant and that the lockdown shock had a bigger effect than the invasion. Considering both types of shocks, the adaptation to previous happiness levels occurred within two to three weeks. Following our findings of similar behaviour in happiness to both types of shocks, the question of whether other types of shocks will have similar effects is posited. Regardless of the length of the adaptation period, understanding the effects of macro-level shocks on happiness is essential for policymakers, as happiness has a spillover effect on other variables such as production, safety and trust.

Spatiotemporal trends in COVID-19 vaccine sentiments on a social media platform and correlations with reported vaccine coverage

Objective

To assess spatiotemporal trends in, and determinants of, the acceptance of coronavirus disease 2019 (COVID-19) vaccination globally, as expressed on the social media platform X (formerly Twitter).

Methods

We collected over 13 million posts on the platform regarding COVID-19 vaccination made between November 2020 and March 2022 in 90 languages. Multilingual deep learning XLM-RoBERTa models annotated all posts using an annotation framework after being fine-tuned on 8125 manually annotated, English-language posts. The annotation results were used to assess spatiotemporal trends in COVID-19 vaccine acceptance and confidence as expressed by platform users in 135 countries and territories. We identified associations between spatiotemporal trends in vaccine acceptance and country-level characteristics and public policies by using univariate and multivariate regression analysis.

Findings

A greater proportion of platform users in the World Health Organization's South-East Asia, Eastern Mediterranean and Western Pacific Regions expressed vaccine acceptance than users in the rest of the world. Countries in which a greater proportion of platform users expressed vaccine acceptance had higher COVID-19 vaccine coverage rates. Trust in government was also associated with greater vaccine acceptance. Internationally, vaccine acceptance and confidence declined among platform users as: (i) vaccination eligibility was extended to adolescents; (ii) vaccine supplies became sufficient; (iii) nonpharmaceutical interventions were relaxed; and (iv) global reports on adverse events following vaccination appeared.

Conclusion

Social media listening could provide an effective and expeditious means of informing public health policies during pandemics, and could supplement existing public health surveillance approaches in addressing global health issues.

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.

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

BACKGROUND

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines.

OBJECTIVE

This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets.

METHODS

We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques.

RESULTS

Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period.

CONCLUSIONS

This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine-related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity.

Artificial Intelligence and Public Health: Evaluating ChatGPT Responses to Vaccination Myths and Misconceptions

Artificial intelligence (AI) tools, such as ChatGPT, are the subject of intense debate regarding their possible applications in contexts such as health care. This study evaluates the Correctness, Clarity, and Exhaustiveness of the answers provided by ChatGPT on the topic of vaccination. The World Health Organization's 11 "myths and misconceptions" about vaccinations were administered to both the free (GPT-3.5) and paid version (GPT-4.0) of ChatGPT. The AI tool's responses were evaluated qualitatively and quantitatively, in reference to those myth and misconceptions provided by WHO, independently by two expert Raters. The agreement between the Raters was significant for both versions (p of K < 0.05). Overall, ChatGPT responses were easy to understand and 85.4% accurate although one of the questions was misinterpreted. Qualitatively, the GPT-4.0 responses were superior to the GPT-3.5 responses in terms of Correctness, Clarity, and Exhaustiveness (Δ = 5.6%, 17.9%, 9.3%, respectively). The study shows that, if appropriately questioned, AI tools can represent a useful aid in the health care field. However, when consulted by non-expert users, without the support of expert medical advice, these tools are not free from the risk of eliciting misleading responses. Moreover, given the existing social divide in information access, the improved accuracy of answers from the paid version raises further ethical issues.

Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy

The paper proposes a methodology based on Natural Language Processing (NLP) and Sentiment Analysis (SA) to get insights into sentiments and opinions toward COVID-19 vaccination in Italy. The studied dataset consists of vaccine-related tweets published in Italy from January 2021 to February 2022. In the considered period, 353,217 tweets have been analyzed, obtained after filtering 1,602,940 tweets with the word "vaccin". A main novelty of the approach is the categorization of opinion holders in four classes, Common users, Media, Medicine, Politics, obtained by applying NLP tools, enhanced with large-scale domain-specific lexicons, on the short bios published by users themselves. Feature-based sentiment analysis is enriched with an Italian sentiment lexicon containing polarized words, expressing semantic orientation, and intensive words which give cues to identify the tone of voice of each user category. The results of the analysis highlighted an overall negative sentiment along all the considered periods, especially for the Common users, and a different attitude of opinion holders towards specific important events, such as deaths after vaccination, occurring in some days of the examined 14 months.

COVID-19 Vaccines Status, Acceptance and Hesitancy among Maintenance Hemodialysis Patients: A Cross-Sectional Study and the Implications for Pakistan and Beyond

COVID-19 vaccine hesitancy continues to be a widespread problem in Pakistan due to various conspiracy beliefs, myths and misconceptions. Since the hemodialysis population is at a higher risk of contracting infections, we sought to investigate the current COVID-19 immunization status and reasons for any vaccine hesitancy among these patients in Pakistan. This cross-sectional study was conducted among maintenance hemodialysis patients at six hospitals in the Punjab Province of Pakistan. Data were collected anonymously using a questionnaire. A total of 399 hemodialysis patients took part in the survey, the majority of them were male (56%) and aged 45-64 years. A calculated 62.4% of the patients reported receiving at least one dose of the COVID-19 vaccine. Of those vaccinated (249), 73.5% had received two doses and 16.9% had received a booster dose. The most common reasons for vaccination were "being aware they were at high risk" (89.6%), "fear of getting infected" (89.2%) and "willingness to fight against COVID-19-pandemic" (83.9%). Of the 150 patients who had not yet been vaccinated, only 10 showed a willingness to take the COVID-19 vaccine. The major reasons for refusal included "COVID-19 is not a real problem" (75%), the "corona vaccine is a conspiracy (72.1%)" and "I don't need the vaccine" (60.7%). Our study revealed that only 62% patients receiving hemodialysis were partially or completely vaccinated against COVID-19. Consequently, there is a need to initiate aggressive approaches to educate this high-risk population in order to address their concerns with vaccine safety and efficacy as well as correct current myths and misconceptions to improve the COVID-19 immunization status in this population.

A year of pandemic: Levels, changes and validity of well-being data from Twitter. Evidence from ten countries

We use daily happiness scores (Gross National Happiness (GNH)) to illustrate how happiness changed throughout 2020 in ten countries across Europe and the Southern hemisphere. More frequently and regularly available than survey data, the GNH reveals how happiness sharply declined at the onset of the pandemic and lockdown, quickly recovered, and then trended downward throughout much of the year in Europe. GNH is derived by applying sentiment and emotion analysis-based on Natural Language Processing using machine learning algorithms-to Twitter posts (tweets). Using a similar approach, we generate another 11 variables: eight emotions and three new context-specific variables, in particular: trust in national institutions, sadness in relation to loneliness, and fear concerning the economy. Given the novelty of the dataset, we use multiple methods to assess validity. We also assess the correlates of GNH. The results indicate that GNH is negatively correlated with new COVID-19 cases, containment policies, and disgust and positively correlated with staying at home, surprise, and generalised trust. Altogether the analyses indicate tools based on Big Data, such as the GNH, offer relevant data that often fill information gaps and can valuably supplement traditional tools. In this case, the GNH results suggest that both the severity of the pandemic and containment policies negatively correlated with happiness.

Mistrust in government and COVID-19 vaccination acceptance in Nigeria: investigating the indirect roles of attitudes towards vaccination

BACKGROUND

Research shows that trust in government is associated with the acceptance of COVID-19 vaccination. However, there is no empirical evidence suggesting the pathway by which this association is formed. This study examines how dimensional attitudes towards vaccination explain the relationship between mistrust in government and COVID-19 vaccination acceptance.

METHODS

The study was an online cross-sectional survey involving 1026 adults (of which 58.9% are female) resident in Nigeria with a mean age of 26.09 (±8.46) years. Data were collected using structured questionnaires assessing the level of mistrust in government, dimensional attitudes towards vaccination, and acceptance to be vaccinated for COVID-19. Structural equation modeling was used to analyze data.

RESULTS

Results show that 56.8% of participants mistrust the government, while COVID-19 vaccination acceptance rate was 28.2%. Mistrust in government was significantly associated with low acceptance of COVID-19 vaccination. Furthermore, mistrust in the government was predictive of negative attitudes towards COVID-19 vaccination which include worries about unforeseen future effects of vaccines, mistrust of vaccine benefits (MVB), concerns about commercial profiteering (CCP), and preference for natural immunity. The outcomes of indirect effect analyses indicated that mistrust in government was associated with high mistrust in vaccine benefits (MVB) and increased concerns about commercial profiteering (CCP), which in turn lead to low acceptance of COVID-19 vaccination.

CONCLUSIONS

Mistrust in the government was high and was coupled with low vaccination acceptance. It is important to initiate culturally relevant awareness programs aiming at combating false notions about COVID-19 vaccination such as MVB and CCP arising from mistrust in government.

Changes in Attitudes toward COVID-19 Vaccination and Vaccine Uptake during Pandemic

The epidemic control approach was based on non-pharmacological measures in the first year of the COVID-19 pandemic, followed by vaccine uptake in the second year. Vaccine uptake depends on the individual attitude toward vaccination. The aim was to assess the changes in attitudes regarding COVID-19 vaccine protection during the pandemic and to determine the vaccination uptake concerning these attitudes. A panel study on COVID-19 vaccine attitudes and vaccination against COVID-19 was conducted in Belgrade, Serbia. The first survey was carried out in May−June 2020, and the second survey was organized in August−September 2021. During the baseline testing performed in 2020, 64.4% of respondents believed that the future vaccine against COVID-19 could protect against the COVID-19 disease, while 9.7% thought that it could not, and 25.9% were unsure. One year later, in the second survey, the percentage of participants with positive attitudes was slightly lower (64.7% vs. 62.5%). However, negative attitudes turned positive in 34% of cases, and 28.9% became unsure about vaccine protection (p < 0.001). Out of the 390 participants included in the study, 79.7% were vaccinated against COVID-19 until follow-up. There is a statistically significant difference in vaccination uptake compared to the baseline attitude about the protection of the COVID-19 vaccine. The main finding of our study is that the majority of participants who were vaccine hesitant during the baseline testing changed their opinion during the follow-up period. Additionally, the baseline attitude about the protection of the COVID-19 vaccine has been shown to be a potential determinant of vaccination uptake.

Development and initial validation of the attitudes toward face mask use scale (ATFMUS)

Despite availability of instruments for measuring attitudes towards mask use, the psychometric properties of many available instruments are not adequately established which limits their research usefulness across contexts. In three studies, we developed the Attitudes Towards Face Mask Use Scale (ATFMUS) in three phases: item generation, scale development, and scale evaluation. Phase one and two were addressed in study 1 while phase three was addressed in studies 2 and 3. In Study 1, a combined online and pen-and-paper sample of 174 (78% university students) completed a questionnaire with 19 items regarding attitudes towards face mask use derived from theory, previous research, and experience. Responses were subjected to item reduction analysis, exploratory factor analysis and reliability analysis. In Study 2, a student sample of 674 (70.5% high school) completed the new scale together with measures of COVID-19 related anxiety and obsession, personality, affect, social media use, and social desirability. Data from the ATFMUS were analyzed using confirmatory factor analysis and pertinent revisions done. The ATFMUS was then validated using correlation analyses, measurement invariance analyses, and known-group comparisons. In study 3, two samples of university students from Ghana (n = 242) and Kenya (n = 199) were involved in testing the cross-country invariance of the ATFMUS. The results reveal that the 5-item ATFMUS is a reliable and valid scale for assessing attitudes towards face mask use. Invariance analysis revealed that the ATFMUS is fair to use across participants of different age, level of education, and countries. The scale is also sensitive to participants' actual use of face masks as well as their beliefs about COVID-19 and efficacy of the facemasks. This study offers a foundation for further psychometric evaluation of the ATFMUS.

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.

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.