Popular attitudes toward vaccination in the post-COVID-19 period: a social media analysis

Popular attitudes toward vaccination in the post-COVID-19 period: a social media analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Popular attitudes toward vaccination in the post-COVID-19 period: a social media analysis Yavuz Selim Balcıoğlu, Özlem KARATANA, Erkut Altındağ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6431026/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The COVID-19 pandemic triggered unprecedented global vaccination campaigns while simultaneously fueling misinformation and vaccine hesitancy. This study analyzed 77,171 English-language tweets related to vaccination collected between December 2020 and May 2021 to understand how attitudes toward vaccination evolved during this critical period and may persist into the post-pandemic era. Employing a mixed-methods approach combining sentiment analysis, thematic classification, and demographic analysis, we identified concerning trends that could affect vaccination acceptance beyond COVID-19. Results revealed a significant decline in positive sentiment (18.3–10.9%) and increase in negative sentiment (9.1–14.6%) over the study period, with a critical inflection point occurring in February 2021. Trust in institutions emerged as the most frequently discussed theme, with initially strong positive sentiment that decreased considerably by May 2021. Childhood vaccination demonstrated a dramatic increase in negative sentiment, rising from 6.7–43.3% by April 2021. Furthermore, users with larger follower counts were found to contribute more negative content, amplifying skepticism. The study identified eight key misinformation categories, including claims about DNA alteration, government control, and 5G connectivity. Interpreted through the Health Belief Model, Social Amplification of Risk Framework, and Institutional Trust Theory, the findings suggest that vaccine distrust may extend to routine immunizations. The results emphasize the urgency of tailored communication strategies to rebuild public trust in vaccines in the post-pandemic world. Humanities/Health humanities Social science/Science technology and society Vaccine hesitancy Social media analysis COVID-19 Institutional trust Childhood vaccination Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The COVID-19 pandemic triggered unprecedented worldwide vaccination campaigns but simultaneously fueled the spread of misinformation and vaccine hesitancy. As the World Health Organization declared an end to COVID-19 as a public health emergency in May 2023, understanding how attitudes toward vaccination have evolved and persisted is crucial for future public health initiatives. This research contributes to the goals outlined in United Nations Sustainable Development Goal 3 by providing actionable insights on rebuilding public trust in vaccines and health institutions. Understanding vaccine attitudes in the post-pandemic period demands a shift from static, survey-based insights toward dynamic, real-time social data. Social media platforms, particularly Twitter, have emerged as powerful arenas where public sentiment, misinformation, and trust in institutions intersect and rapidly evolve (Melton et al., 2022 ). While initial optimism accompanied early vaccine rollouts, digitally mediated discourse increasingly reflected politicization, skepticism, and the amplification of risk (Skafle et al., 2022 ). As risk narratives circulated and gained visibility through influential users, even scientifically unfounded claims—such as DNA alteration or governmental control—achieved viral traction, complicating institutional outreach efforts (Shaaban et al., 2022 ). Consequently, addressing vaccine hesitancy now requires not only combating falsehoods, but also restoring institutional credibility in fragmented and emotionally charged digital ecosystems (Lyu et al., 2022 ). Based on the identified gaps in current understanding of post-pandemic vaccination attitudes, this study addresses the following research questions: How did public sentiment toward vaccines evolve during the initial COVID-19 vaccination campaigns, and what trajectories might persist into the post-pandemic period? What specific themes and concerns regarding vaccination emerged during the pandemic that may continue to influence attitudes toward routine immunizations? How do demographic factors, including user influence level and geographic location, correlate with vaccination sentiment and potential hesitancy? To what extent do attitudes vary between different vaccine types, and how might these differences impact future vaccination campaigns? What specific misinformation narratives demonstrated resilience throughout the pandemic, potentially affecting long-term public trust in vaccines and health authorities? Addressing these questions requires a thorough understanding of the existing scholarly discourse on vaccine hesitancy, misinformation dynamics, and the socio-digital environments in which public attitudes are formed and reinforced. By situating this study within the broader theoretical and empirical landscape, the following section reviews the relevant literature to contextualize the rise of vaccine-related skepticism during and after the COVID-19 pandemic. Literature Review The COVID-19 pandemic has affected public views and perceptions of health and health measures. Especially with COVID-19 vaccines, misinformation and conspiracy theories about the safety and efficacy of vaccination have appeared on social media platforms, disrupting the success of countries' vaccination programs (Benson et al. 2024 ; Romer, Jamieson 2020 ). This is why the World Health Organization (WHO) listed vaccine hesitancy as one of the top ten threats to global health in 2019 (Wilson, Wiysonge 2020 ). Vaccines are the best public health strategy to prevent disease, but the growing against vaccinations movement has brought renewed attention to the need to convince people to get vaccinated. Anti-vaccination sentiment existed before the COVID-19 pandemic (Pullan, Dey 2021 ), increased during the pandemic (Saldarriaga, 2023 ), and has now emerged as a public health issue that must be tackled (Galagali et al. 2022 ). Vaccine hesitancy generally includes a lack of proper information, adherence to religious beliefs, or misunderstandings of vaccines (Erokhin et al. 2022 ; Ullah et al. 2021 ). The increased use of social media platforms has changed the landscape of public discourse on various topics, including health and vaccination during the COVID-19 pandemic (Ennab et al. 2022 ). For example, during the SARS, Ebola, and H1N1 pandemics, there was an increase in the use of social media and compliance with protective measures such as hand washing, wearing masks, and social distancing (Kim et al. 2019 ), while at the same time increasing fears about vaccines by spreading conspiracy theories and misinformation about vaccines (Lee et al. 2022 ). While vaccine supporters share scientific studies on social media, relying on modern medicine on issues related to pandemia (Hammarlin 2023), against vaccinations have been effective in spreading fears about vaccine safety (Wilson, Wiysonge 2020 ). This situation turns into a conflict within the society. Examining social media to understand the threat posed by against vaccinations on social media, to understand online discussions/public opinion on vaccines, and to explore how these can influence society is critical for the effectiveness of vaccination programs (Puri et al. 2020 ). Social media platforms enable the sharing of information about vaccines and can increase vaccine hesitancy and against vaccinations (UNICEF 2013 ). A review of the literature shows that while there is data on COVID-19 vaccine hesitancy (Vicario et al. 2024 ), analysis of vaccine hesitancy to all vaccines after the COVID-19 pandemic is limited. Given the proliferation of misinformation and differing perspectives shared on social media, understanding how these platforms influence vaccine support or hesitancy is crucial to effectively address public health challenges. The aim of this study is to analyze public discourse on social media about vaccines in the aftermath of the COVID-19 pandemic and to investigate the factors that cause vaccine hesitancy in this context using Social Network Analysis (SNA). Conceptual Framework of the Study Much like public health interventions, acceptance and uptake of vaccines are dependent on whether populations place their trust in the vaccine itself (trust in the product; belief and perceptions), the institution that provides the vaccine (institutional trust), and the professionals who communicate and administer it (inter-personal trust) ( Larson et al. 2018 ). The conceptual framework of the study consisted of a Health Belief Model (HBM), Social Amplification of Risk Framework (SARF) and Institutional Trust Theory, appropriately adjusted as an organizing framework to characterize and interpret complex vaccine hesitancy experiences related to general vaccines post COVID-19. Public trust in vaccines is shaped by individual perceptions of risk, institutional legitimacy, and media-amplified narratives (Larson et al., 2018 ; Tyler, 2006 ; Kasperson, 2014 ). The Health Belief Model explains how perceived severity, susceptibility, and benefit-harm evaluations drive vaccine behavior (Champion & Skinner, 2008 ; Carpenter, 2010 ). Simultaneously, the Social Amplification of Risk Framework reveals how risk signals are distorted through digital communication, fueling public anxiety (Larson et al., 2022 ; Skafle et al., 2022 ). Institutional Trust Theory further accounts for how declining confidence in authorities correlates with rising hesitancy and resistance (Seddig et al., 2022 ; Krastev et al., 2023 ). Health Belief Model (HBM) According to the health belief model (HBM), people's specific beliefs, namely perceived severity and susceptibility of the disease and the perceived benefits and risks of the vaccine, relate to health behaviors (Harrison et al. 1998). This model describes individuals engaging in an internal decision-making process weighing the pros and cons of taking a particular action (getting vaccinated) and cognitively assessing the severity of the health threat they face and the perceived benefits or harms of taking a particular action in relation to that health threat. This individual risk assessment is influenced by many factors, including the perceived risk of a disease, available information about the transmissibility and severity of the disease, the source of the information (whether it is a reliable source), their personal environment, cultural beliefs and the social media in which they live and interact (Carpenter 2010 ). Specifically, the HBM has been one of the most common theoretical frameworks used to explain vaccine hesitancy in various settings (Chen et al. 2011 ; Donadiki et al. 2014 ). In the context of vaccination, the HBM suggests that vaccine uptake is dependent on factors such as a person’s beliefs about the severity of the disease, their susceptibility to it, as well as the vaccine’s efficacy and safety (Krastev et al. 2023 ). Social Amplification of Risk Framework (SARF) SARF intended to guide and integrate various investigations into how perceptions may be influenced, change, and affect behavior, and how behavioral change leads to potential risk consequences (Kasperson 2014 ). SARF identifies three stages for analysis: Stage 1: A risk-related event or other news item relating to a risk is communicated and transformed through communication channels; the communication is further transformed as it is interpreted by an individual or organization and interpretation may alter individual or organizational perceptions. Stage 2: The individual's or organization's altered perceptions of risk influence their behavior. Stage 3: Individual or organizational behavior will affect risk experiences; there may be direct impacts on the risk associated with the initiating event; there may also be secondary and tertiary impacts on other risks. The current state of vaccine hesitancy is fully in line with the SARF (Larson et al. 2022 ): public perceptions differ greatly and are very different from mainstream expert perceptions: there has been significant resistance to vaccination, resulting in increased risks of infection and increased economic and social risks. Communication about vaccines and their real and imagined risks can influence perceptions that strongly influence vaccine acceptance or refusal behavior. Increased refusal can significantly increase the technical risk of getting sick, both for individuals themselves and for the population around them. They also show that changing perceptions and behaviors can open up many more possibilities for ripple effects (Larson et al. 2022 ). Institutional Trust Theory Institutional trust refers to citizens' beliefs that institutions (e.g., government, the justice system, the medical establishment, science) act in a predictable, equitable, fair, and transparent manner and in ways that serve the citizens’ interests (e.g., Fukuyama 1995; Putnam et al. 1993). Trust in institutions is related to perceived legitimacy of institutions (Khodyakov 2007 ) and compliance with formal and informal norms (Tyler 2006 ). Thus, as evidenced in COVID-19 vaccinations, people who trusted their institutions were associated with positive attitudes toward vaccines and a higher willingness to be vaccinated, while institutional distrust was associated with negative attitudes and vaccine hesitancy (Seddig et al. 2022 ; Trent et al. 2022 ). Institutional trust is shaped by perceptions of competence, integrity, and benevolence (Krastev et al., 2023 ; Tyler, 2006 ). During the COVID-19 pandemic, inconsistent messaging and political controversy undermined these trust dimensions, especially on social media platforms where misinformation flourished (Shaaban et al., 2022 ; Skafle et al., 2022 ). Numerous studies confirm that declining institutional trust correlates strongly with vaccine hesitancy and resistance to mandates (Seddig et al., 2022 ; Anyiam-Osigwe et al., 2024 ). Rebuilding this trust in the post-pandemic context demands transparent governance and inclusive health communication strategies (Viswanath et al., 2021 ). Methods Data Collection and Preprocessing For this study, we analyzed 77.171 English-language tweets related to vaccination collected between December 2020 and May 2021. The dataset was obtained through Twitter's API using vaccination-related keywords and hashtags, including "#vaccine", "#COVID19", "#Pfizer", "#Moderna", "#AstraZeneca", "#SputnikV", "#Covaxin", "#Sinovac", and general terms like "vaccination", "immunization", and "shot". The temporal distribution of tweets is shown in Table 1, with the highest volume occurring in March 2021 (27.566 tweets) and April 2021 (26.619 tweets), corresponding with widespread vaccine rollout initiatives globally. Month Number of Tweets Percentage of Dataset Dec 2020 1.980 2.57% Jan 2021 3.029 3.93% Feb 2021 10.524 13.64% Mar 2021 27.566 35.72% Apr 2021 26.619 34.49% May 2021 7.453 9.66% Total 77.171 100% Table 1. Temporal Distribution of Tweets Data preprocessing involved several steps: · Removing duplicate tweets · Filtering out non-English content · Cleaning text by removing URLs, special characters, and irrelevant symbols · Normalizing text to lowercase · Extracting relevant metadata (user information, date, engagement metrics) Analytical Framework Our analytical approach followed a mixed-methods design incorporating quantitative sentiment analysis and qualitative thematic classification. Figure 1 presents the conceptual framework guiding our methodology, illustrating the interconnected components of our analytical process. The framework consists of four primary analytical dimensions: · Sentiment Analysis : Examining positive, negative, and neutral attitudes · Thematic Classification : Identifying key concerns and discussion topics · Temporal Analysis : Tracking changes in sentiment and themes over time · Demographic Analysis : Exploring variations across user segments Sentiment Analysis We employed a lexicon-based approach to sentiment analysis, developing a domain-specific dictionary of vaccination-related terms (Doğan et al. 2025). The sentiment classification system used: · Positive sentiment indicators : Words and phrases including "effective," "safe," "protect," "hope," "good," "successful," "progress," "excited," "happy," "thankful," "grateful," "confident," "trust," "science," "breakthrough," "solution," "heal," "cure," "benefit," and "recommend" · Negative sentiment indicators : Words and phrases including "unsafe," "dangerous," "risk," "side effect," "death," "fear," "scary," "experimental," "untested," "mistrust," "conspiracy," "lie," "corrupt," "refuse," "against," "poison," "harm," "microchip," "forced," "mandate," "skeptic," "hesitant," "bad," "terrible," and "adverse" Each tweet was scored based on the presence of these indicators, with tweets containing more positive than negative indicators classified as positive, more negative than positive as negative, and equal counts or neither as neutral. To validate this approach, we manually coded a random sample of 500 tweets, achieving 83% agreement between human coders and our automated classification. Thematic Classification We developed a comprehensive coding scheme to identify prevalent themes in vaccination discourse, focusing particularly on topics relevant to post-pandemic attitudes. The coding scheme included: · Safety and side effects : References to vaccine safety, adverse reactions, or long-term effects · Efficacy : Discussion of vaccine effectiveness and protection · Mandates and freedom : Content related to vaccine requirements, restrictions, or personal choice · Institutional trust : References to trust/distrust in governments, health authorities, or pharmaceutical companies · Misinformation : Identified inaccurate claims about vaccines · Science and research : Discussion of clinical trials, data, or scientific evidence · Child vaccination : Content specifically addressing vaccines for children or adolescents · Post-pandemic perspectives : Forward-looking statements about vaccination in the future Each tweet could be assigned multiple theme codes as appropriate. A subset of tweets (n=1.000) was manually coded by two researchers to establish intercoder reliability (Cohen's κ = 0.79), after which a rule-based classification system was applied to the entire dataset. Demographic Analysis We leveraged available metadata to segment tweets along two key dimensions: 1. User influence : Categorized based on follower count. o Micro-influencers (<100 followers) o Small accounts (100-999 followers) o Medium accounts (1.000-9.999 followers) o Large accounts (10.000-99.999 followers) o Very large accounts (≥100.000 followers) 2. Geographic location : Extracted from user profile location fields and normalized to country/region level where possible. Vaccine-Specific Analysis To understand attitudes toward different vaccine brands, we identified tweets referencing specific vaccines through keyword matching and hashtag analysis. The most frequently mentioned vaccines were Moderna, Covaxin, Sputnik V, Pfizer, Sinovac, AstraZeneca, and Johnson & Johnson. Misinformation Detection We developed a taxonomy of vaccine misinformation based on established frameworks in the literature, identifying eight primary categories of misinformation narratives: · DNA/RNA alterations · Government surveillance/control · 5G connectivity · Rushed development/inadequate testing · Microchip implantation · Fertility concerns · Depopulation agenda · Magnetism claims Using a keyword-based approach combined with contextual analysis, we identified and categorized tweets containing these misinformation narratives. Post-Pandemic Perspective Identification To specifically address attitudes that may persist into the post-pandemic period, we identified tweets containing forward-looking language, including terms such as "future," "long-term," "ongoing," "annual," "post-pandemic," "post-COVID," "new normal," and similar phrases. These tweets were subjected to additional analysis to understand perspectives specifically relevant to the post-pandemic context. Data Analysis Tools The analysis was conducted using Python 3.9 with several specialized libraries: · Natural Language Processing: NLTK and spaCy · Data manipulation: Pandas and NumPy · Statistical analysis: SciPy and StatsModels · Visualization: Matplotlib and Seaborn All code and analysis procedures were documented and version-controlled to ensure reproducibility. Results Temporal Trends in Vaccination Sentiment Our analysis revealed substantial shifts in vaccination sentiment throughout the study period. Figure 2 depicts the month-by-month evolution of positive and negative sentiment from December 2020 to May 2021. Overall, we observed a consistent decline in positive sentiment from 18.3% in December 2020 to 10.9% in May 2021, representing a 40.4% relative decrease. Concurrently, negative sentiment demonstrated a more volatile pattern but showed an overall increase from 9.1% in December 2020 to 14.6% in May 2021, representing a 60.4% relative increase. The remaining tweets maintained neutral sentiment, which constituted the majority (73.7%) of the dataset. A notable inflection point occurred in February 2021, when negative sentiment (14.9%) surpassed positive sentiment (14.5%) for the first time in the study period. This crossover coincided with increased international reporting of potential side effects and growing public debate about vaccine mandates. Sentiment trends revealed clear surges in public anxiety following announcements of side effects associated with specific vaccines. On Twitter, this anxiety translated into persistent negativity, particularly around mandates and trust in pharmaceutical companies. The platform’s fast-paced, emotionally charged environment appears to amplify fear-based content more rapidly than factual updates (Melton et al., 2022 ). The sentiment trajectory demonstrates three distinct phases: Initial Optimism (December 2020 - January 2021): Characterized by high positive sentiment (18.3–18.1%) and relatively low negative sentiment (9.1–10.8%). Skepticism Emergence (February 2021 - March 2021): Marked by the crossover of sentiment lines with negative sentiment outpacing positive. Entrenchment (April 2021 - May 2021): Showing stabilization of the new sentiment pattern with consistently higher negative than positive sentiment. Thematic Analysis of Vaccination Discourse Our thematic analysis identified seven major themes that are particularly relevant to post-pandemic vaccination attitudes. Table 2 presents these themes with their prevalence and associated sentiment patterns. Table 2 Major Themes in Vaccination Discourse Theme Tweets % of Dataset Positive Sentiment Negative Sentiment Neutral Sentiment Trust in institutions 1.803 2.34% 39.7% 9.0% 51.3% Childhood vaccination 948 1.23% 12.8% 17.2% 70.0% Personal freedom 597 0.77% 12.9% 12.2% 74.9% Vaccine hesitancy 446 0.58% 5.6% 45.7% 48.7% Mandatory policies 440 0.57% 10.7% 14.1% 75.2% Future pandemics 214 0.28% 18.2% 9.3% 72.5% Routine vaccination 190 0.25% 10.0% 15.8% 74.2% Trust in institutions emerged as the most frequently discussed theme (2.34% of all tweets) and exhibited the highest positive sentiment (39.7%). This finding suggests a resilient core of institutional trust despite the overall declining trend in positive sentiment. Conversely, vaccine hesitancy-related tweets, while less common (0.58%), showed the highest negative sentiment (45.7%), indicating intense negativity within this subset of discussions. The temporal evolution of key themes revealed significant patterns relevant to post-pandemic attitudes: Trust in institutions : Initially strong positive sentiment decreased considerably by May 2021 (from 45.5% in December 2020 to 27.5% in May 2021) Childhood vaccination : Demonstrated a dramatic increase in negative sentiment in April 2021 (43.3%, compared to 6.7% in December 2020) Mandatory policies : Consistently generated more negative than positive sentiment throughout the period Discussions about COVID-19 vaccines evolved from medical safety to political autonomy as the pandemic progressed. Users with prior negative experiences with public institutions exhibited higher resistance to vaccine messaging. These dynamics highlight how political trust and perceived institutional competence shape social media discourse on public health (Lyu et al., 2020). Vaccine hesitancy : Exhibited increasingly negative sentiment, reaching 59.2% negative in May 2021 Misinformation Analysis Our investigation identified eight distinct categories of vaccine misinformation. Figure 3 displays the prevalence of these narratives within the dataset. DNA alteration claims constituted the most prevalent misinformation category (659 tweets, 0.85% of dataset), followed by government control narratives (573 tweets, 0.74%) and 5G connectivity claims (518 tweets, 0.67%). While representing a relatively small portion of the overall discourse, these misinformation narratives demonstrated persistence throughout the study period and generated substantial engagement, with an average of 20.3 retweets per misinformation tweet compared to 5.7 retweets for non-misinformation content. Temporal analysis of misinformation revealed that while the absolute volume of misinformation increased proportionally with the overall tweet volume, the relative percentage remained consistent at approximately 2.7–3.1% of monthly tweets. This consistency suggests these narratives had become established within vaccination discourse rather than representing temporary concerns. Demographic Differences in Vaccination Attitudes Influence Level Analysis We categorized users based on follower count to examine how influence level correlates with vaccination sentiment. Table 3 presents the sentiment distribution across different influence tiers. Table 3 Sentiment Distribution by User Influence Level Influence Tier Number of Tweets Positive Sentiment Negative Sentiment Neutral Sentiment Micro (< 100) 18.602 13.3% 11.0% 75.7% Small (100–999) 27.602 14.2% 11.6% 74.2% Medium (1.000-9.999) 21.269 11.9% 15.3% 72.8% Large (10.000-99.999) 6.061 13.3% 16.2% 70.5% Very large (≥ 100.000) 3.637 13.3% 16.2% 70.5% A notable pattern emerged: users with larger followings (medium, large, and very large accounts) expressed higher levels of negative sentiment (15.3–16.2%) compared to users with smaller followings (11.0-11.6%). This finding suggests that influential voices may be disproportionately amplifying negative vaccination narratives. Geographic Analysis Geographic segmentation based on user-provided location data revealed regional variations in vaccination sentiment. Figure 4 illustrates sentiment patterns across the top 10 identifiable locations. Users from India showed the highest levels of positive sentiment toward vaccines (19.3%), while users from the United States exhibited higher negative sentiment (15.8%) compared to other regions. These differences likely reflect varying experiences with vaccine rollout, differing political contexts, and region-specific concerns. Vaccine-Specific Attitudes Our analysis identified substantial differences in sentiment across vaccine types. Figure 5 depicts sentiment patterns for the most frequently mentioned vaccines. Moderna was the most frequently mentioned vaccine (17.585 mentions), followed by Covaxin (9.584 mentions) and Sputnik V (7.561 mentions). Sentiment analysis revealed that Sputnik V generated the highest proportion of positive sentiment (15.7%), while AstraZeneca exhibited the highest negative sentiment (22.1%), likely reflecting widely reported concerns about rare blood clotting side effects. The relationship between sentiment and vaccine country of origin was also noteworthy: vaccines developed in Western countries (Pfizer, Moderna, Johnson & Johnson) showed different sentiment patterns compared to those developed in non-Western countries (Sputnik V, Sinovac, Covaxin). This suggests geopolitical factors may influence public perception of vaccines, potentially affecting future international vaccination efforts. Post-Pandemic Perspectives We identified 65 tweets (0.08% of the dataset) specifically discussing post-pandemic vaccination attitudes. While representing a small subset, these tweets provide valuable insights into forward-looking perspectives. Figure 6 shows the sentiment distribution within this group. Post-pandemic perspective tweets demonstrated higher negative sentiment (26.2%) compared to the overall dataset (13.1%) and lower positive sentiment (16.9% vs. 13.2% overall). This suggests that forward-looking discussions about vaccination contain more concerns than optimism. Thematic analysis of post-pandemic tweets revealed several recurrent topics: Global distribution equity (6.2% of post-pandemic tweets) Trust in health authorities (3.1%) Future vaccination schedules (1.5%) Vaccine hesitancy concerns (1.5%) Notably, discussions about children's vaccination and vaccine mandates were absent from the post-pandemic perspective tweets, despite their prominence in the broader dataset. Children's Vaccination Concerns Tweets related to childhood vaccination (n = 1.214, 1.57% of dataset) showed distinctive patterns relevant to post-pandemic vaccination attitudes. Figure 7 illustrates the temporal evolution of sentiment regarding childhood vaccination. In early 2021 (January-March), sentiment toward childhood vaccination remained relatively positive (14.6–18.9%). However, April 2021 marked a dramatic shift, with negative sentiment increasing sharply to 43.3%, significantly higher than the overall dataset average. This coincided with expanded clinical trials of COVID-19 vaccines in pediatric populations and growing public debate about school vaccination requirements. Qualitative analysis of negative childhood vaccination tweets revealed three predominant concerns: Safety and long-term effects (56.3% of negative childhood vaccination tweets) Necessity given lower COVID-19 risk in children (23.1%) Parental choice and autonomy (20.6%) Highly Engaging Content Analysis Analysis of the most engaging tweets (highest combined retweets and favorites) revealed patterns that may illuminate which vaccination narratives gain traction. The top 10 most engaging tweets (n = 10) garnered a combined 151.339 interactions (retweets + favorites), representing 24.8% of all engagement in the dataset. Among these highly engaging tweets, 50% contained neutral sentiment, 30% positive, and 20% negative. However, the engagement per tweet was highest for negative content (average 18.456 interactions) compared to positive (12.877) or neutral (11.238), suggesting that negative content, while less common, generates more engagement when it does appear. Thematically, the most engaging tweets focused on: Vaccine production and availability announcements (40%) Reports of successfully receiving vaccination (30%) Side effect discussions (20%) Policy updates (10%) This engagement pattern suggests that while practical information about vaccine availability dominated the discourse, emotional content (both positive vaccination experiences and negative side effect reports) generated disproportionate engagement. Discussion This study provides a comprehensive analysis of vaccination attitudes during a pivotal period of the COVID-19 pandemic, revealing patterns that may have significant implications for post-pandemic vaccination efforts. By examining nearly 77.000 tweets from December 2020 to May 2021, we have identified several concerning trends that could influence vaccination acceptance beyond COVID-19. Our findings are discussed within the context of established theoretical frameworks and recent literature. Evolving Sentiment and the Social Amplification of Risk The temporal analysis of vaccination sentiment revealed a troubling trajectory: a consistent decline in positive sentiment from 18.3% in December 2020 to 10.9% in May 2021, coupled with an increase in negative sentiment from 9.1–14.6% during the same period. This pattern aligns with the Social Amplification of Risk Framework (SARF), which explains how risk perceptions can be magnified through social and cultural processes (Skafle et al. 2022 ). The critical inflection point observed in February 2021—when negative sentiment surpassed positive sentiment for the first time—coincided with increased reporting of potential side effects and growing public debate about vaccine mandates. This crossover may represent a tipping point in public discourse, where the amplification of perceived risks began to outweigh the communication of benefits. This finding is consistent with research by Shaaban et al. ( 2022 ), who found that social media platforms significantly contributed to the spread of vaccine concerns during the pandemic, with approximately 36% of individuals relying on social media for vaccine information. Our analysis further demonstrates that once negative narratives gain traction, they can persist and potentially influence attitudes toward vaccination more broadly, creating what Viswanath et al. ( 2021 ) describe as an "infodemic" that challenges public health communication efforts. Institutional Trust and Its Erosion Trust in institutions emerged as the most frequently discussed theme in our dataset, with initially strong positive sentiment that decreased considerably by May 2021 (from 45.5–27.5%). This erosion of trust is particularly concerning, as it may have implications beyond COVID-19 vaccines. Anyiam-Osigwe et al. ( 2024 ) highlighted the significance of institutional trust in their analysis of post-COVID vaccination attitudes among healthcare workers, finding that decreased trust in authorities was associated with increased vaccine hesitancy for routine immunizations. The decline in institutional trust observed in our study may be explained through Institutional Trust Theory, which posits that trust is built through perceived competence, integrity, and benevolence of institutions. The rapid development and deployment of COVID-19 vaccines, combined with changing public health guidance and politicization of the pandemic response, may have challenged all three of these dimensions. This is reflected in our data showing that "trust in institutions" tweets, while exhibiting the highest positive sentiment (39.7%) among all themes, still demonstrated a concerning downward trend over time. Demographic Differences and Information Ecosystems Our analysis revealed significant demographic variations in vaccination sentiment, with users with larger followings (medium, large, and very large accounts) expressing higher levels of negative sentiment (15.3–16.2%) compared to users with smaller followings (11.0-11.6%). This finding suggests that influential voices may be disproportionately amplifying negative vaccination narratives, creating what Viswanath et al. ( 2021 ) term "echo chambers" where skepticism is reinforced and amplified. The geographic differences in sentiment, with users from India showing higher levels of positive sentiment (19.3%) compared to users from the United States (with higher negative sentiment at 15.8%), align with findings by Shaaban et al. ( 2022 ), who identified substantial cross-cultural variations in vaccine acceptance rates across 24 countries. These differences likely reflect varying experiences with vaccine rollout, different political contexts, and region-specific concerns, highlighting the need for culturally tailored approaches to vaccine communication. Children's Vaccination: A Particular Concern Perhaps the most alarming finding in our study was the sharp increase in negative sentiment regarding childhood vaccination, which rose dramatically from 6.7% in December 2020 to 43.3% in April 2021. This shift coincided with expanded clinical trials of COVID-19 vaccines in pediatric populations and growing public debate about school vaccination requirements. The predominant concerns identified in our qualitative analysis—safety and long-term effects (56.3%), necessity given lower COVID-19 risk in children (23.1%), and parental choice and autonomy (20.6%)—mirror those found by Golder et al. ( 2023 ) in their mixed-methods analysis of vaccination hesitancy during pregnancy, where safety concerns and the perceived rapid development of vaccines were primary barriers. This finding is particularly concerning because attitudes toward COVID-19 vaccination for children may influence perceptions of routine childhood immunizations. The Health Belief Model helps explain this phenomenon: as parents weigh the perceived benefits against perceived risks of vaccination, the heightened perception of risk associated with COVID-19 vaccines may spill over to other vaccines, potentially threatening the hard-won progress in global childhood immunization programs. Misinformation and Its Persistence Our investigation identified eight distinct categories of vaccine misinformation, with DNA alteration claims, government control narratives, and 5G connectivity claims being the most prevalent. While representing a relatively small portion of the overall discourse (approximately 2.7–3.1% of monthly tweets), these misinformation narratives demonstrated remarkable persistence throughout the study period and generated substantial engagement—an average of 20.3 retweets per misinformation tweet compared to 5.7 retweets for non-misinformation content. This disproportionate amplification of misinformation aligns with findings by Skafle et al. ( 2022 ), who identified similar themes of misinformation in their systematic review, including concerns about DNA alterations, government surveillance/control, and inadequate testing. The persistence of these narratives throughout our study period suggests they had become established within vaccination discourse rather than representing temporary concerns, creating what Nyawa et al. ( 2022 ) describe as "information bubbles" that can be difficult to penetrate with factual information. Implications for Post-Pandemic Vaccination Efforts Our analysis of post-pandemic perspective tweets (0.08% of the dataset) revealed higher negative sentiment (26.2%) compared to the overall dataset (13.1%), suggesting that forward-looking discussions about vaccination contain more concerns than optimism. This finding, though based on a small subset of data, raises important questions about the potential long-term impact of the COVID-19 pandemic on vaccination attitudes more broadly. The patterns identified in our study—declining trust in institutions, persistent misinformation, and increasing concerns about childhood vaccination—could threaten routine immunization programs if left unaddressed. This concern is supported by recent research by Anyiam-Osigwe et al. ( 2024 ), who found that the COVID-19 pandemic influenced healthcare workers' attitudes toward routine flu vaccination, leading to what they termed "vaccine fatigue" and heightened concerns about autonomy in vaccination decisions. Engagement Patterns and Communication Strategies Our analysis of highly engaging content revealed that while negative content was less common, it generated more engagement when it did appear (average 18.456 interactions) compared to positive (12.877) or neutral (11.238) content. This engagement pattern aligns with the Social Amplification of Risk Framework, which suggests that negative information often spreads more widely and rapidly than positive information. This finding has important implications for public health communication strategies. While practical information about vaccine availability dominated the discourse, emotional content—both positive vaccination experiences and negative side effect reports—generated disproportionate engagement. This suggests that effective communication strategies should incorporate both factual information and emotional appeals, potentially leveraging positive personal stories to counterbalance the emotional impact of negative narratives. Theoretical Integration and Practical Implications The integration of our empirical findings with established theoretical frameworks—the Social Amplification of Risk Framework, the Health Belief Model, and Institutional Trust Theory—reveals how negative perceptions about COVID-19 vaccines may transfer to routine immunization programs. The observed shift from initial optimism to increasing skepticism, particularly evident in the February 2021 crossover point where negative sentiment first exceeded positive, represents a potential inflection point in public attitudes toward vaccination more broadly. For public health practitioners, our findings highlight the need for tailored communication strategies that address specific concerns identified in this research. Particularly important is rebuilding institutional trust, addressing safety concerns about childhood vaccination, and developing effective approaches to counter persistent misinformation narratives. Geographic and demographic variations in sentiment suggest that one-size-fits-all approaches to vaccine communication may be ineffective, supporting the need for culturally and demographically targeted strategies. The role of influential social media users in amplifying negative narratives suggests that engaging digital opinion leaders in positive vaccination messaging could be an effective strategy. Additionally, the finding that emotional content generates disproportionate engagement indicates that combining factual information with compelling personal stories may be more effective than purely information-based approaches. Conclusion This study provides a comprehensive examination of vaccination attitudes during a critical period of the COVID-19 pandemic, offering insights that may help anticipate and address challenges in the post-pandemic vaccination landscape. By analyzing nearly 77,000 tweets from December 2020 to May 2021, we have identified concerning trends that could affect vaccination acceptance beyond COVID-19, including declining positive sentiment, persistent misinformation narratives, influential negative voices, and growing concerns about childhood vaccination. The integration of our empirical findings with established theoretical frameworks—the Social Amplification of Risk Framework, the Health Belief Model, and Institutional Trust Theory—reveals how negative perceptions about COVID-19 vaccines may transfer to routine immunization programs. The observed shift from initial optimism to increasing skepticism, particularly evident in the February 2021 crossover point where negative sentiment first exceeded positive, represents a potential inflection point in public attitudes toward vaccination more broadly. Particularly concerning is the sharp increase in negative sentiment regarding childhood vaccination in April 2021, which could have lasting implications for pediatric immunization programs. The prevalence of safety concerns and the amplification of misinformation through influential social media users present ongoing challenges for public health communication. Geographic and demographic variations in sentiment suggest that tailored, context-specific approaches will be necessary to rebuild vaccine confidence. These findings support United Nations Sustainable Development Goal 3 by providing actionable insights for public health officials working to ensure healthy lives and promote well-being for all ages. By understanding the specific concerns, misinformation narratives, and sentiment patterns identified in this research, health authorities can develop more effective strategies to rebuild public trust in vaccines and health institutions, both for ongoing COVID-19 vaccination efforts and for routine immunization programs in the post-pandemic era. Future research should continue to monitor vaccination attitudes as the world transitions from pandemic to post-pandemic status, particularly examining whether the patterns identified here persist, intensify, or resolve over time. Additional studies focusing specifically on attitudes toward childhood vaccination, the role of influential social media users in shaping discourse, and effective counter-misinformation strategies would further contribute to our understanding of this complex issue. Ultimately, the path to rebuilding and maintaining strong vaccine confidence in the post-COVID world requires acknowledging and addressing the legitimate concerns that have emerged during the pandemic, while simultaneously working to combat misinformation and restore trust in health authorities. This research provides a foundation for these efforts by identifying key challenges and opportunities in the evolving landscape of vaccination attitudes. Declarations Author Contribution Conceptualization, Y.S.B., Ö.K. and E.A.; methodology, Y.S.B.; software and algorithm implementation, Y.S.B.; formal analysis, Y.S.B.; data collection, Y.S.B.and Ö.K.; writing—original draft preparation, Ö.K., E.A.; writing—review and editing, Y.S.B., Ö.K. and E.A.; supervision, Ö.K. and E.A. All authors have read and agreed to the published version of the manuscript. Data Availability The Twitter data used in this study cannot be publicly shared due to privacy concerns, Twitter's Terms of Service, and ethical considerations regarding user consent. The dataset contains personally identifiable information and potentially sensitive content from users who did not explicitly consent to participate in this research. Furthermore, Twitter's (now X's) Developer Agreement restricts the redistribution of tweet content in its original form.Instead, we provide the following:1.Detailed methodological information: Our manuscript contains comprehensive details about data collection methods, preprocessing steps, and analytical approaches to enable other researchers to replicate our study design.2.Aggregated statistics: Summary statistics, sentiment distribution, and thematic categorization results are available in the manuscript tables and figures.3.Coding framework: The sentiment analysis lexicon and thematic classification codebook used in this study can be made available upon reasonable request to the corresponding author, subject to a data usage agreement.4.Tweet IDs: Upon request and subject to appropriate data usage agreements, we can provide a list of tweet IDs that would allow qualified researchers to reconstruct the dataset using Twitter's API, in accordance with Twitter's Developer Policy. This approach is consistent with established practices in social media research that balance open science principles with privacy concerns.For inquiries regarding the dataset, please contact the corresponding author. References Anyiam-Osigwe A, Katangwe-Chigamba T, Scott S et al (2024) A psychosocial critique of the consequences of the COVID-19 pandemic on UK care home staff attitudes to the flu vaccination: A qualitative longitudinal study. Vaccines 12(12):1437. https://doi.org/10.3390/vaccines12121437 Benson BR, Rahman SA, Bleasdale J et al (2024) Trusted Information Sources About the COVID-19 Vaccine Vary in Underserved Communities. J Community Health 49:598–605. https://doi.org/10.1007/s10900-023-01319-0 Carpenter CJ (2010) A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun 25(8):661–669 Champion VL, Skinner CS (2008) The health belief model. Health behavior and health education: Theory, research, and practice 4: 45–65 Chen MF, Wang RH, Schneider JK et al (2011) Using the health belief model to understand caregiver factors influencing childhood influenza vaccinations. J Commun Health Nurs 28(1):29–40 Doğan B, Balcioglu YS, Elçi M (2025) Multidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic. Kybernetes 54(4):2414–2456 Donadiki EM, Jiménez-García R, Hernández-Barrera V et al (2014) Health Belief Model applied to non-compliance with HPV vaccine among female university students. Public Health 128:268–273 Ennab F, Babar MS, Khan AR et al (2022) Implications of social media misinformation on COVID-19 vaccine confidence among pregnant women in Africa. Clin Epidemiol Glob Health 14:100981 Erokhin D, Yosipof A, Komendantova N (2022) COVID-19 Conspiracy Theories Discussion on Twitter. Social Media + Soc 8(4). https://doi.org/10.1177/20563051221126051 Galagali PM, Kinikar AA, Kumar VS (2022) Vaccine hesitancy: obstacles and challenges. Curr Pediatr Rep 10:241–248 Golder S, McRobbie-Johnson ACE, Klein A et al (2023) Social media and COVID-19 vaccination hesitancy during pregnancy: A mixed methods analysis. BJOG: Int J Obstet Gynecol 130(7):750–758. https://doi.org/10.1111/1471-0528.17481 Harrison JA, Mullen PD, Green LW (1992) A meta-analysis of studies of the health belief model with adults. Health Educ Res 7(1):107–116 Kasperson RE (2014) Four questions for risk communication. J Risk Res 17(10):1233–1239. https://doi.org/10.1080/13669877.2014.90020 Khodyakov D (2007) Trust as a process: A three-dimensional approach. Sociology 41(1):115–132 Kim L, Fast SM, Markuzon N (2019) Incorporating media data into a model of infectious disease transmission. PLoS ONE 4(2):e0197646. 10.1371/journal.pone.0197646 Krastev S, Krajden O, Vang ZM et al (2023) Institutional trust is a distinct construct related to vaccine hesitancy and refusal. BMC Public Health 23:2481. https://doi.org/10.1186/s12889-023-17345-5 Larson HJ, Clarke RM, Jarrett C et al (2018) Measuring trust in vaccination: A systematic review. Hum vaccines immunotherapeutics 14(7):1599–1609 Larson HJ, Lin L, Goble R (2022) Vaccines and the social amplification of risk. Risk Anal 42(7):1409–1422 Lee SK, Sun J, Jang S et al (2022) Misinformation of COVID-19 vaccines and vaccine hesitancy. Sci Rep 12:13681 Lyu H, Wang J, Wu W, Duong V, Zhang X, Dye TD, Luo J (2022) Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination. Intell Med 2(01):1–12 Matenga TFL, Zulu JM, Moonzwe Davis L et al (2022) Motivating factors for and barriers to the COVID-19 vaccine uptake: a review of social media data in Zambia. Cogent Public Health 9:2059201 Melton CA, White BM, Davis RL, Bednarczyk RA, Shaban-Nejad A (2022) Fine-tuned sentiment analysis of covid-19 vaccine–related social media data: Comparative study. J Med Internet Res, 24(10), e40408 Nyawa S, Tchuente D, Fosso-Wamba S (2022) COVID-19 vaccine hesitancy: A social media analysis using deep learning. Ann Oper Res 1–39. https://doi.org/10.1007/s10479-022-04792-3 O’Donohoe S (2010) Netnography: doing ethnographic research online. Int J Advertising 29:328–330. 10.2501/S026504871020118X Pullan S, Dey M (2021) Vaccine hesitancy and anti-vaccination in the time of COVID-19: A Google Trends analysis. Vaccine 39(14):1877–1881 Puri N, Coomes EA, Haghbayan H et al (2020) Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases. Hum vaccines immunotherapeutics 16(11):2586–2593 Romer D, Jamieson KH (2020) Conspiracy theories as barriers to controlling the spread of COVID-19 in the US. Soc Sci Med 263:113356 Saldarriaga EM (2023) Using machine learning to identify COVID-19 vaccine-hesitancy predictors in the USA. BMJ Public Health 1(1):e000456. 10.1136/BMJPH-2023-000456 Seddig D, Maskileyson D, Davidov E et al (2022) Correlates of COVID-19 vaccination intentions: Attitudes, institutional trust, fear, conspiracy beliefs, and vaccine skepticism. Soc Sci Med 302:114981 Shaaban R, Ghazy RM, Elsherif F et al (2022) COVID-19 vaccine acceptance among social media users: A content analysis, multi-continent study. Int J Environ Res Public Health 19(9):5737. https://doi.org/10.3390/ijerph19095737 Skafle I, Nordahl-Hansen A, Quintana DS et al (2022) Misinformation about COVID-19 vaccines on social media: Rapid review. J Med Internet Res 24(8):e37367. https://doi.org/10.2196/37367 Softić A, Omeragić E, Kondža M et al (2023) Knowledge and attitudes regarding Covid-19 vaccination among medical and non-medical students in Bosnia and Herzegovina. Acta Med Academica 52(1):1–12. https://doi.org/10.5644/ama2006-124.396 Trent M, Seale H, Chughtai AA et al (2022) Trust in government, intention to vaccinate and COVID-19 vaccine hesitancy: A comparative survey of five large cities in the United States, United Kingdom, and Australia. Vaccine 40(17):2498–2505 Tyler TR (2006) Why people obey the law. Princeton University Press Ullah I, Khan KS, Tahir MJ et al (2021) Myths and conspiracy theories on vaccines and COVID-19: Potential effect on global vaccine refusals. Vacunas 22(2):93–97 UNICEF (2013) Tracking anti vaccination sentiment in Eastern European social media networks. New York: UNICEF. https://www.unicef.org/eca/media/1556/file/Tracking-anti-vaccination-sentiment-in-astern-European-social-media-networks.pdf Vicario CM, Mucciardi M, Faraone G et al (2024) Individual predictors of vaccine hesitancy in the Italian post COVID-19 pandemic era. Hum Vaccines Immunotherapeutics 20(1). https://doi.org/10.1080/21645515.2024.2306677 Viswanath K, Bekalu M, Dhawan D et al (2021) Individual and social determinants of COVID-19 vaccine uptake. BMC Public Health 21(1):818. https://doi.org/10.1186/s12889-021-10862-1 Wilson SL, Wiysonge C (2020) Social media and vaccine hesitancy. BMJ Global Health 5(10):e004206 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6431026","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489950708,"identity":"5fe705df-ec7f-4054-80ec-c877fd1b606b","order_by":0,"name":"Yavuz Selim Balcıoğlu","email":"","orcid":"","institution":"Doğuş University","correspondingAuthor":false,"prefix":"","firstName":"Yavuz","middleName":"Selim","lastName":"Balcıoğlu","suffix":""},{"id":489950709,"identity":"e31927c3-1da9-4df9-bf4c-e9e26f5134ec","order_by":1,"name":"Özlem KARATANA","email":"","orcid":"","institution":"Doğuş University","correspondingAuthor":false,"prefix":"","firstName":"Özlem","middleName":"","lastName":"KARATANA","suffix":""},{"id":489950711,"identity":"1c665bc5-74a9-4a92-92fa-50acc8189d74","order_by":2,"name":"Erkut Altındağ","email":"data:image/png;base64,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","orcid":"","institution":"Doğuş University","correspondingAuthor":true,"prefix":"","firstName":"Erkut","middleName":"","lastName":"Altındağ","suffix":""}],"badges":[],"createdAt":"2025-04-11 20:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6431026/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6431026/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87519098,"identity":"21c3ae95-c184-40d0-af0e-5bc317bc3fda","added_by":"auto","created_at":"2025-07-24 17:13:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331804,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework for Analyzing Post-COVID Vaccination Attitudes\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/ba34b9c412e3a276be65a82f.png"},{"id":87519101,"identity":"44d6b541-d563-4d2c-83c3-69e06a9882d4","added_by":"auto","created_at":"2025-07-24 17:13:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216225,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Evolution of Vaccination Sentiment (Dec 2020 - May 2021)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/d367f69932d1192812f157ed.png"},{"id":87519428,"identity":"52647e70-0cf5-4b65-ab0a-90c63c568be7","added_by":"auto","created_at":"2025-07-24 17:21:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164769,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of Vaccine Misinformation Categories\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/cd651e2a5f715ad643801450.png"},{"id":87519100,"identity":"9b1a913b-5ddf-448d-8e83-98033c0ec2d0","added_by":"auto","created_at":"2025-07-24 17:13:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129055,"visible":true,"origin":"","legend":"\u003cp\u003eSentiment Patterns Across Geographic Regions\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/002860d6030fabccce57d300.png"},{"id":87519422,"identity":"45d99c5c-299a-4a9b-870c-40ec1bcdbd23","added_by":"auto","created_at":"2025-07-24 17:21:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145364,"visible":true,"origin":"","legend":"\u003cp\u003eSentiment Patterns Across Vaccine Types\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/8bef923c67524ebe654fa292.png"},{"id":87519112,"identity":"4875faa4-95f9-4eba-97cf-dd0d2cd6d0e1","added_by":"auto","created_at":"2025-07-24 17:13:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":271136,"visible":true,"origin":"","legend":"\u003cp\u003eSentiment and Topics in Post-Pandemic Perspective Tweets\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/1734aa1f450eb56de1615417.png"},{"id":87519890,"identity":"adcad83d-d1d4-4730-8e87-bad497090e7e","added_by":"auto","created_at":"2025-07-24 17:29:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":195168,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Evolution of Sentiment Regarding Children's Vaccination\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/c5e627bb9256c421fe874ff6.png"},{"id":95546724,"identity":"9d0ddc6f-6d6b-482e-abf9-a6c4cd72fab9","added_by":"auto","created_at":"2025-11-10 12:38:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2718164,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6431026/v1/e4600efd-8c54-4754-aa47-4d0a113a6af5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Popular attitudes toward vaccination in the post-COVID-19 period: a social media analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic triggered unprecedented worldwide vaccination campaigns but simultaneously fueled the spread of misinformation and vaccine hesitancy. As the World Health Organization declared an end to COVID-19 as a public health emergency in May 2023, understanding how attitudes toward vaccination have evolved and persisted is crucial for future public health initiatives. This research contributes to the goals outlined in United Nations Sustainable Development Goal 3 by providing actionable insights on rebuilding public trust in vaccines and health institutions.\u003c/p\u003e\u003cp\u003eUnderstanding vaccine attitudes in the post-pandemic period demands a shift from static, survey-based insights toward dynamic, real-time social data. Social media platforms, particularly Twitter, have emerged as powerful arenas where public sentiment, misinformation, and trust in institutions intersect and rapidly evolve (Melton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While initial optimism accompanied early vaccine rollouts, digitally mediated discourse increasingly reflected politicization, skepticism, and the amplification of risk (Skafle et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As risk narratives circulated and gained visibility through influential users, even scientifically unfounded claims\u0026mdash;such as DNA alteration or governmental control\u0026mdash;achieved viral traction, complicating institutional outreach efforts (Shaaban et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, addressing vaccine hesitancy now requires not only combating falsehoods, but also restoring institutional credibility in fragmented and emotionally charged digital ecosystems (Lyu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on the identified gaps in current understanding of post-pandemic vaccination attitudes, this study addresses the following research questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow did public sentiment toward vaccines evolve during the initial COVID-19 vaccination campaigns, and what trajectories might persist into the post-pandemic period?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat specific themes and concerns regarding vaccination emerged during the pandemic that may continue to influence attitudes toward routine immunizations?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do demographic factors, including user influence level and geographic location, correlate with vaccination sentiment and potential hesitancy?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo what extent do attitudes vary between different vaccine types, and how might these differences impact future vaccination campaigns?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat specific misinformation narratives demonstrated resilience throughout the pandemic, potentially affecting long-term public trust in vaccines and health authorities?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAddressing these questions requires a thorough understanding of the existing scholarly discourse on vaccine hesitancy, misinformation dynamics, and the socio-digital environments in which public attitudes are formed and reinforced. By situating this study within the broader theoretical and empirical landscape, the following section reviews the relevant literature to contextualize the rise of vaccine-related skepticism during and after the COVID-19 pandemic.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe COVID-19 pandemic has affected public views and perceptions of health and health measures. Especially with COVID-19 vaccines, misinformation and conspiracy theories about the safety and efficacy of vaccination have appeared on social media platforms, disrupting the success of countries' vaccination programs (Benson et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Romer, Jamieson \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is why the World Health Organization (WHO) listed vaccine hesitancy as one of the top ten threats to global health in 2019 (Wilson, Wiysonge \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Vaccines are the best public health strategy to prevent disease, but the growing against vaccinations movement has brought renewed attention to the need to convince people to get vaccinated. Anti-vaccination sentiment existed before the COVID-19 pandemic (Pullan, Dey \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), increased during the pandemic (Saldarriaga, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and has now emerged as a public health issue that must be tackled (Galagali et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Vaccine hesitancy generally includes a lack of proper information, adherence to religious beliefs, or misunderstandings of vaccines (Erokhin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe increased use of social media platforms has changed the landscape of public discourse on various topics, including health and vaccination during the COVID-19 pandemic (Ennab et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, during the SARS, Ebola, and H1N1 pandemics, there was an increase in the use of social media and compliance with protective measures such as hand washing, wearing masks, and social distancing (Kim et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while at the same time increasing fears about vaccines by spreading conspiracy theories and misinformation about vaccines (Lee et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While vaccine supporters share scientific studies on social media, relying on modern medicine on issues related to pandemia (Hammarlin 2023), against vaccinations have been effective in spreading fears about vaccine safety (Wilson, Wiysonge \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This situation turns into a conflict within the society. Examining social media to understand the threat posed by against vaccinations on social media, to understand online discussions/public opinion on vaccines, and to explore how these can influence society is critical for the effectiveness of vaccination programs (Puri et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Social media platforms enable the sharing of information about vaccines and can increase vaccine hesitancy and against vaccinations (UNICEF \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA review of the literature shows that while there is data on COVID-19 vaccine hesitancy (Vicario et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), analysis of vaccine hesitancy to all vaccines after the COVID-19 pandemic is limited. Given the proliferation of misinformation and differing perspectives shared on social media, understanding how these platforms influence vaccine support or hesitancy is crucial to effectively address public health challenges. The aim of this study is to analyze public discourse on social media about vaccines in the aftermath of the COVID-19 pandemic and to investigate the factors that cause vaccine hesitancy in this context using Social Network Analysis (SNA).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eConceptual Framework of the Study\u003c/h2\u003e\u003cp\u003eMuch like public health interventions, acceptance and uptake of vaccines are dependent on whether populations place their trust in the vaccine itself (trust in the product; belief and perceptions), the institution that provides the vaccine (institutional trust), and the professionals who communicate and administer it (inter-personal trust) ( Larson et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The conceptual framework of the study consisted of a Health Belief Model (HBM), Social Amplification of Risk Framework (SARF) and Institutional Trust Theory, appropriately adjusted as an organizing framework to characterize and interpret complex vaccine hesitancy experiences related to general vaccines post COVID-19. Public trust in vaccines is shaped by individual perceptions of risk, institutional legitimacy, and media-amplified narratives (Larson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tyler, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kasperson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Health Belief Model explains how perceived severity, susceptibility, and benefit-harm evaluations drive vaccine behavior (Champion \u0026amp; Skinner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Carpenter, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Simultaneously, the Social Amplification of Risk Framework reveals how risk signals are distorted through digital communication, fueling public anxiety (Larson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Skafle et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Institutional Trust Theory further accounts for how declining confidence in authorities correlates with rising hesitancy and resistance (Seddig et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Krastev et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHealth Belief Model (HBM)\u003c/h3\u003e\n\u003cp\u003eAccording to the health belief model (HBM), people's specific beliefs, namely perceived severity and susceptibility of the disease and the perceived benefits and risks of the vaccine, relate to health behaviors (Harrison et al. 1998). This model describes individuals engaging in an internal decision-making process weighing the pros and cons of taking a particular action (getting vaccinated) and cognitively assessing the severity of the health threat they face and the perceived benefits or harms of taking a particular action in relation to that health threat. This individual risk assessment is influenced by many factors, including the perceived risk of a disease, available information about the transmissibility and severity of the disease, the source of the information (whether it is a reliable source), their personal environment, cultural beliefs and the social media in which they live and interact (Carpenter \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Specifically, the HBM has been one of the most common theoretical frameworks used to explain vaccine hesitancy in various settings (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Donadiki et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the context of vaccination, the HBM suggests that vaccine uptake is dependent on factors such as a person\u0026rsquo;s beliefs about the severity of the disease, their susceptibility to it, as well as the vaccine\u0026rsquo;s efficacy and safety (Krastev et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSocial Amplification of Risk Framework (SARF)\u003c/h3\u003e\n\u003cp\u003eSARF intended to guide and integrate various investigations into how perceptions may be influenced, change, and affect behavior, and how behavioral change leads to potential risk consequences (Kasperson \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). SARF identifies three stages for analysis: Stage 1: A risk-related event or other news item relating to a risk is communicated and transformed through communication channels; the communication is further transformed as it is interpreted by an individual or organization and interpretation may alter individual or organizational perceptions. Stage 2: The individual's or organization's altered perceptions of risk influence their behavior. Stage 3: Individual or organizational behavior will affect risk experiences; there may be direct impacts on the risk associated with the initiating event; there may also be secondary and tertiary impacts on other risks. The current state of vaccine hesitancy is fully in line with the SARF (Larson et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e): public perceptions differ greatly and are very different from mainstream expert perceptions: there has been significant resistance to vaccination, resulting in increased risks of infection and increased economic and social risks. Communication about vaccines and their real and imagined risks can influence perceptions that strongly influence vaccine acceptance or refusal behavior. Increased refusal can significantly increase the technical risk of getting sick, both for individuals themselves and for the population around them. They also show that changing perceptions and behaviors can open up many more possibilities for ripple effects (Larson et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eInstitutional Trust Theory\u003c/h3\u003e\n\u003cp\u003eInstitutional trust refers to citizens' beliefs that institutions (e.g., government, the justice system, the medical establishment, science) act in a predictable, equitable, fair, and transparent manner and in ways that serve the citizens\u0026rsquo; interests (e.g., Fukuyama 1995; Putnam et al. 1993). Trust in institutions is related to perceived legitimacy of institutions (Khodyakov \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and compliance with formal and informal norms (Tyler \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Thus, as evidenced in COVID-19 vaccinations, people who trusted their institutions were associated with positive attitudes toward vaccines and a higher willingness to be vaccinated, while institutional distrust was associated with negative attitudes and vaccine hesitancy (Seddig et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Trent et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Institutional trust is shaped by perceptions of competence, integrity, and benevolence (Krastev et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tyler, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). During the COVID-19 pandemic, inconsistent messaging and political controversy undermined these trust dimensions, especially on social media platforms where misinformation flourished (Shaaban et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Skafle et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Numerous studies confirm that declining institutional trust correlates strongly with vaccine hesitancy and resistance to mandates (Seddig et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anyiam-Osigwe et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rebuilding this trust in the post-pandemic context demands transparent governance and inclusive health communication strategies (Viswanath et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collection and Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this study, we analyzed 77.171 English-language tweets related to vaccination collected between December 2020 and May 2021. The dataset was obtained through Twitter\u0026apos;s API using vaccination-related keywords and hashtags, including \u0026quot;#vaccine\u0026quot;, \u0026quot;#COVID19\u0026quot;, \u0026quot;#Pfizer\u0026quot;, \u0026quot;#Moderna\u0026quot;, \u0026quot;#AstraZeneca\u0026quot;, \u0026quot;#SputnikV\u0026quot;, \u0026quot;#Covaxin\u0026quot;, \u0026quot;#Sinovac\u0026quot;, and general terms like \u0026quot;vaccination\u0026quot;, \u0026quot;immunization\u0026quot;, and \u0026quot;shot\u0026quot;. The temporal distribution of tweets is shown in Table 1, with the highest volume occurring in March 2021 (27.566 tweets) and April 2021 (26.619 tweets), corresponding with widespread vaccine rollout initiatives globally.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Tweets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage of Dataset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eDec 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eJan 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eFeb 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e10.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e13.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMar 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e27.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e35.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eApr 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e26.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e34.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMay 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e9.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e77.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eTemporal Distribution of Tweets\u003c/p\u003e\n\u003cp\u003eData preprocessing involved several steps:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Removing duplicate tweets\u003c/p\u003e\n\u003cp\u003e\u0026middot; Filtering out non-English content\u003c/p\u003e\n\u003cp\u003e\u0026middot; Cleaning text by removing URLs, special characters, and irrelevant symbols\u003c/p\u003e\n\u003cp\u003e\u0026middot; Normalizing text to lowercase\u003c/p\u003e\n\u003cp\u003e\u0026middot; Extracting relevant metadata (user information, date, engagement metrics)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analytical approach followed a mixed-methods design incorporating quantitative sentiment analysis and qualitative thematic classification. Figure 1 presents the conceptual framework guiding our methodology, illustrating the interconnected components of our analytical process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe framework consists of four primary analytical dimensions:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSentiment Analysis\u003c/strong\u003e: Examining positive, negative, and neutral attitudes\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eThematic Classification\u003c/strong\u003e: Identifying key concerns and discussion topics\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eTemporal Analysis\u003c/strong\u003e: Tracking changes in sentiment and themes over time\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eDemographic Analysis\u003c/strong\u003e: Exploring variations across user segments\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSentiment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed a lexicon-based approach to sentiment analysis, developing a domain-specific dictionary of vaccination-related terms (Doğan et al. 2025). The sentiment classification system used:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003ePositive sentiment indicators\u003c/strong\u003e: Words and phrases including \u0026quot;effective,\u0026quot; \u0026quot;safe,\u0026quot; \u0026quot;protect,\u0026quot; \u0026quot;hope,\u0026quot; \u0026quot;good,\u0026quot; \u0026quot;successful,\u0026quot; \u0026quot;progress,\u0026quot; \u0026quot;excited,\u0026quot; \u0026quot;happy,\u0026quot; \u0026quot;thankful,\u0026quot; \u0026quot;grateful,\u0026quot; \u0026quot;confident,\u0026quot; \u0026quot;trust,\u0026quot; \u0026quot;science,\u0026quot; \u0026quot;breakthrough,\u0026quot; \u0026quot;solution,\u0026quot; \u0026quot;heal,\u0026quot; \u0026quot;cure,\u0026quot; \u0026quot;benefit,\u0026quot; and \u0026quot;recommend\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eNegative sentiment indicators\u003c/strong\u003e: Words and phrases including \u0026quot;unsafe,\u0026quot; \u0026quot;dangerous,\u0026quot; \u0026quot;risk,\u0026quot; \u0026quot;side effect,\u0026quot; \u0026quot;death,\u0026quot; \u0026quot;fear,\u0026quot; \u0026quot;scary,\u0026quot; \u0026quot;experimental,\u0026quot; \u0026quot;untested,\u0026quot; \u0026quot;mistrust,\u0026quot; \u0026quot;conspiracy,\u0026quot; \u0026quot;lie,\u0026quot; \u0026quot;corrupt,\u0026quot; \u0026quot;refuse,\u0026quot; \u0026quot;against,\u0026quot; \u0026quot;poison,\u0026quot; \u0026quot;harm,\u0026quot; \u0026quot;microchip,\u0026quot; \u0026quot;forced,\u0026quot; \u0026quot;mandate,\u0026quot; \u0026quot;skeptic,\u0026quot; \u0026quot;hesitant,\u0026quot; \u0026quot;bad,\u0026quot; \u0026quot;terrible,\u0026quot; and \u0026quot;adverse\u0026quot;\u003c/p\u003e\n\u003cp\u003eEach tweet was scored based on the presence of these indicators, with tweets containing more positive than negative indicators classified as positive, more negative than positive as negative, and equal counts or neither as neutral. To validate this approach, we manually coded a random sample of 500 tweets, achieving 83% agreement between human coders and our automated classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThematic Classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a comprehensive coding scheme to identify prevalent themes in vaccination discourse, focusing particularly on topics relevant to post-pandemic attitudes. The coding scheme included:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eSafety and side effects\u003c/strong\u003e: References to vaccine safety, adverse reactions, or long-term effects\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eEfficacy\u003c/strong\u003e: Discussion of vaccine effectiveness and protection\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eMandates and freedom\u003c/strong\u003e: Content related to vaccine requirements, restrictions, or personal choice\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eInstitutional trust\u003c/strong\u003e: References to trust/distrust in governments, health authorities, or pharmaceutical companies\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eMisinformation\u003c/strong\u003e: Identified inaccurate claims about vaccines\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eScience and research\u003c/strong\u003e: Discussion of clinical trials, data, or scientific evidence\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eChild vaccination\u003c/strong\u003e: Content specifically addressing vaccines for children or adolescents\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003ePost-pandemic perspectives\u003c/strong\u003e: Forward-looking statements about vaccination in the future\u003c/p\u003e\n\u003cp\u003eEach tweet could be assigned multiple theme codes as appropriate. A subset of tweets (n=1.000) was manually coded by two researchers to establish intercoder reliability (Cohen\u0026apos;s \u0026kappa; = 0.79), after which a rule-based classification system was applied to the entire dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe leveraged available metadata to segment tweets along two key dimensions:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eUser influence\u003c/strong\u003e: Categorized based on follower count.\u003c/p\u003e\n\u003cp\u003eo Micro-influencers (\u0026lt;100 followers)\u003c/p\u003e\n\u003cp\u003eo Small accounts (100-999 followers)\u003c/p\u003e\n\u003cp\u003eo Medium accounts (1.000-9.999 followers)\u003c/p\u003e\n\u003cp\u003eo Large accounts (10.000-99.999 followers)\u003c/p\u003e\n\u003cp\u003eo Very large accounts (\u0026ge;100.000 followers)\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eGeographic location\u003c/strong\u003e: Extracted from user profile location fields and normalized to country/region level where possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVaccine-Specific Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand attitudes toward different vaccine brands, we identified tweets referencing specific vaccines through keyword matching and hashtag analysis. The most frequently mentioned vaccines were Moderna, Covaxin, Sputnik V, Pfizer, Sinovac, AstraZeneca, and Johnson \u0026amp; Johnson.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMisinformation Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a taxonomy of vaccine misinformation based on established frameworks in the literature, identifying eight primary categories of misinformation narratives:\u003c/p\u003e\n\u003cp\u003e\u0026middot; DNA/RNA alterations\u003c/p\u003e\n\u003cp\u003e\u0026middot; Government surveillance/control\u003c/p\u003e\n\u003cp\u003e\u0026middot; 5G connectivity\u003c/p\u003e\n\u003cp\u003e\u0026middot; Rushed development/inadequate testing\u003c/p\u003e\n\u003cp\u003e\u0026middot; Microchip implantation\u003c/p\u003e\n\u003cp\u003e\u0026middot; Fertility concerns\u003c/p\u003e\n\u003cp\u003e\u0026middot; Depopulation agenda\u003c/p\u003e\n\u003cp\u003e\u0026middot; Magnetism claims\u003c/p\u003e\n\u003cp\u003eUsing a keyword-based approach combined with contextual analysis, we identified and categorized tweets containing these misinformation narratives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-Pandemic Perspective Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo specifically address attitudes that may persist into the post-pandemic period, we identified tweets containing forward-looking language, including terms such as \u0026quot;future,\u0026quot; \u0026quot;long-term,\u0026quot; \u0026quot;ongoing,\u0026quot; \u0026quot;annual,\u0026quot; \u0026quot;post-pandemic,\u0026quot; \u0026quot;post-COVID,\u0026quot; \u0026quot;new normal,\u0026quot; and similar phrases. These tweets were subjected to additional analysis to understand perspectives specifically relevant to the post-pandemic context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis was conducted using Python 3.9 with several specialized libraries:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Natural Language Processing: NLTK and spaCy\u003c/p\u003e\n\u003cp\u003e\u0026middot; Data manipulation: Pandas and NumPy\u003c/p\u003e\n\u003cp\u003e\u0026middot; Statistical analysis: SciPy and StatsModels\u003c/p\u003e\n\u003cp\u003e\u0026middot; Visualization: Matplotlib and Seaborn\u003c/p\u003e\n\u003cp\u003eAll code and analysis procedures were documented and version-controlled to ensure reproducibility.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eTemporal Trends in Vaccination Sentiment\u003c/h2\u003e\u003cp\u003eOur analysis revealed substantial shifts in vaccination sentiment throughout the study period. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the month-by-month evolution of positive and negative sentiment from December 2020 to May 2021. Overall, we observed a consistent decline in positive sentiment from 18.3% in December 2020 to 10.9% in May 2021, representing a 40.4% relative decrease. Concurrently, negative sentiment demonstrated a more volatile pattern but showed an overall increase from 9.1% in December 2020 to 14.6% in May 2021, representing a 60.4% relative increase. The remaining tweets maintained neutral sentiment, which constituted the majority (73.7%) of the dataset. A notable inflection point occurred in February 2021, when negative sentiment (14.9%) surpassed positive sentiment (14.5%) for the first time in the study period. This crossover coincided with increased international reporting of potential side effects and growing public debate about vaccine mandates. Sentiment trends revealed clear surges in public anxiety following announcements of side effects associated with specific vaccines. On Twitter, this anxiety translated into persistent negativity, particularly around mandates and trust in pharmaceutical companies. The platform\u0026rsquo;s fast-paced, emotionally charged environment appears to amplify fear-based content more rapidly than factual updates (Melton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sentiment trajectory demonstrates three distinct phases:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInitial Optimism\u003c/b\u003e (December 2020 - January 2021): Characterized by high positive sentiment (18.3\u0026ndash;18.1%) and relatively low negative sentiment (9.1\u0026ndash;10.8%).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSkepticism Emergence\u003c/b\u003e (February 2021 - March 2021): Marked by the crossover of sentiment lines with negative sentiment outpacing positive.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEntrenchment\u003c/b\u003e (April 2021 - May 2021): Showing stabilization of the new sentiment pattern with consistently higher negative than positive sentiment.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eThematic Analysis of Vaccination Discourse\u003c/h2\u003e\u003cp\u003eOur thematic analysis identified seven major themes that are particularly relevant to post-pandemic vaccination attitudes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents these themes with their prevalence and associated sentiment patterns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMajor Themes in Vaccination Discourse\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTweets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of Dataset\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive Sentiment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNegative Sentiment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNeutral Sentiment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrust in institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildhood vaccination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaccine hesitancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMandatory policies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuture pandemics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e72.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoutine vaccination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTrust in institutions emerged as the most frequently discussed theme (2.34% of all tweets) and exhibited the highest positive sentiment (39.7%). This finding suggests a resilient core of institutional trust despite the overall declining trend in positive sentiment. Conversely, vaccine hesitancy-related tweets, while less common (0.58%), showed the highest negative sentiment (45.7%), indicating intense negativity within this subset of discussions.\u003c/p\u003e\u003cp\u003eThe temporal evolution of key themes revealed significant patterns relevant to post-pandemic attitudes:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTrust in institutions\u003c/b\u003e: Initially strong positive sentiment decreased considerably by May 2021 (from 45.5% in December 2020 to 27.5% in May 2021)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eChildhood vaccination\u003c/b\u003e: Demonstrated a dramatic increase in negative sentiment in April 2021 (43.3%, compared to 6.7% in December 2020)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMandatory policies\u003c/b\u003e: Consistently generated more negative than positive sentiment throughout the period Discussions about COVID-19 vaccines evolved from medical safety to political autonomy as the pandemic progressed. Users with prior negative experiences with public institutions exhibited higher resistance to vaccine messaging. These dynamics highlight how political trust and perceived institutional competence shape social media discourse on public health (Lyu et al., 2020).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eVaccine hesitancy\u003c/b\u003e: Exhibited increasingly negative sentiment, reaching 59.2% negative in May 2021\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eMisinformation Analysis\u003c/h2\u003e\u003cp\u003eOur investigation identified eight distinct categories of vaccine misinformation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the prevalence of these narratives within the dataset.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDNA alteration claims constituted the most prevalent misinformation category (659 tweets, 0.85% of dataset), followed by government control narratives (573 tweets, 0.74%) and 5G connectivity claims (518 tweets, 0.67%). While representing a relatively small portion of the overall discourse, these misinformation narratives demonstrated persistence throughout the study period and generated substantial engagement, with an average of 20.3 retweets per misinformation tweet compared to 5.7 retweets for non-misinformation content.\u003c/p\u003e\u003cp\u003eTemporal analysis of misinformation revealed that while the absolute volume of misinformation increased proportionally with the overall tweet volume, the relative percentage remained consistent at approximately 2.7\u0026ndash;3.1% of monthly tweets. This consistency suggests these narratives had become established within vaccination discourse rather than representing temporary concerns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDemographic Differences in Vaccination Attitudes\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003eInfluence Level Analysis\u003c/h2\u003e\u003cp\u003eWe categorized users based on follower count to examine how influence level correlates with vaccination sentiment. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the sentiment distribution across different influence tiers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSentiment Distribution by User Influence Level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluence Tier\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Tweets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Sentiment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative Sentiment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeutral Sentiment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicro (\u0026lt;\u0026thinsp;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall (100\u0026ndash;999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium (1.000-9.999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge (10.000-99.999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery large (\u0026ge;\u0026thinsp;100.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA notable pattern emerged: users with larger followings (medium, large, and very large accounts) expressed higher levels of negative sentiment (15.3\u0026ndash;16.2%) compared to users with smaller followings (11.0-11.6%). This finding suggests that influential voices may be disproportionately amplifying negative vaccination narratives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eGeographic Analysis\u003c/h2\u003e\u003cp\u003eGeographic segmentation based on user-provided location data revealed regional variations in vaccination sentiment. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates sentiment patterns across the top 10 identifiable locations. Users from India showed the highest levels of positive sentiment toward vaccines (19.3%), while users from the United States exhibited higher negative sentiment (15.8%) compared to other regions. These differences likely reflect varying experiences with vaccine rollout, differing political contexts, and region-specific concerns.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eVaccine-Specific Attitudes\u003c/h2\u003e\u003cp\u003eOur analysis identified substantial differences in sentiment across vaccine types. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts sentiment patterns for the most frequently mentioned vaccines. Moderna was the most frequently mentioned vaccine (17.585 mentions), followed by Covaxin (9.584 mentions) and Sputnik V (7.561 mentions). Sentiment analysis revealed that Sputnik V generated the highest proportion of positive sentiment (15.7%), while AstraZeneca exhibited the highest negative sentiment (22.1%), likely reflecting widely reported concerns about rare blood clotting side effects. The relationship between sentiment and vaccine country of origin was also noteworthy: vaccines developed in Western countries (Pfizer, Moderna, Johnson \u0026amp; Johnson) showed different sentiment patterns compared to those developed in non-Western countries (Sputnik V, Sinovac, Covaxin). This suggests geopolitical factors may influence public perception of vaccines, potentially affecting future international vaccination efforts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003ePost-Pandemic Perspectives\u003c/h2\u003e\u003cp\u003eWe identified 65 tweets (0.08% of the dataset) specifically discussing post-pandemic vaccination attitudes. While representing a small subset, these tweets provide valuable insights into forward-looking perspectives. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the sentiment distribution within this group. Post-pandemic perspective tweets demonstrated higher negative sentiment (26.2%) compared to the overall dataset (13.1%) and lower positive sentiment (16.9% vs. 13.2% overall). This suggests that forward-looking discussions about vaccination contain more concerns than optimism.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThematic analysis of post-pandemic tweets revealed several recurrent topics:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGlobal distribution equity (6.2% of post-pandemic tweets)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTrust in health authorities (3.1%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFuture vaccination schedules (1.5%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eVaccine hesitancy concerns (1.5%)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eNotably, discussions about children's vaccination and vaccine mandates were absent from the post-pandemic perspective tweets, despite their prominence in the broader dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eChildren's Vaccination Concerns\u003c/h2\u003e\u003cp\u003eTweets related to childhood vaccination (n\u0026thinsp;=\u0026thinsp;1.214, 1.57% of dataset) showed distinctive patterns relevant to post-pandemic vaccination attitudes. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the temporal evolution of sentiment regarding childhood vaccination. In early 2021 (January-March), sentiment toward childhood vaccination remained relatively positive (14.6\u0026ndash;18.9%). However, April 2021 marked a dramatic shift, with negative sentiment increasing sharply to 43.3%, significantly higher than the overall dataset average. This coincided with expanded clinical trials of COVID-19 vaccines in pediatric populations and growing public debate about school vaccination requirements.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eQualitative analysis of negative childhood vaccination tweets revealed three predominant concerns:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSafety and long-term effects (56.3% of negative childhood vaccination tweets)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNecessity given lower COVID-19 risk in children (23.1%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eParental choice and autonomy (20.6%)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eHighly Engaging Content Analysis\u003c/h2\u003e\u003cp\u003eAnalysis of the most engaging tweets (highest combined retweets and favorites) revealed patterns that may illuminate which vaccination narratives gain traction. The top 10 most engaging tweets (n\u0026thinsp;=\u0026thinsp;10) garnered a combined 151.339 interactions (retweets\u0026thinsp;+\u0026thinsp;favorites), representing 24.8% of all engagement in the dataset.\u003c/p\u003e\u003cp\u003eAmong these highly engaging tweets, 50% contained neutral sentiment, 30% positive, and 20% negative. However, the engagement per tweet was highest for negative content (average 18.456 interactions) compared to positive (12.877) or neutral (11.238), suggesting that negative content, while less common, generates more engagement when it does appear.\u003c/p\u003e\u003cp\u003eThematically, the most engaging tweets focused on:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eVaccine production and availability announcements (40%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReports of successfully receiving vaccination (30%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSide effect discussions (20%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePolicy updates (10%)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis engagement pattern suggests that while practical information about vaccine availability dominated the discourse, emotional content (both positive vaccination experiences and negative side effect reports) generated disproportionate engagement.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive analysis of vaccination attitudes during a pivotal period of the COVID-19 pandemic, revealing patterns that may have significant implications for post-pandemic vaccination efforts. By examining nearly 77.000 tweets from December 2020 to May 2021, we have identified several concerning trends that could influence vaccination acceptance beyond COVID-19. Our findings are discussed within the context of established theoretical frameworks and recent literature.\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eEvolving Sentiment and the Social Amplification of Risk\u003c/h2\u003e\u003cp\u003eThe temporal analysis of vaccination sentiment revealed a troubling trajectory: a consistent decline in positive sentiment from 18.3% in December 2020 to 10.9% in May 2021, coupled with an increase in negative sentiment from 9.1\u0026ndash;14.6% during the same period. This pattern aligns with the Social Amplification of Risk Framework (SARF), which explains how risk perceptions can be magnified through social and cultural processes (Skafle et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The critical inflection point observed in February 2021\u0026mdash;when negative sentiment surpassed positive sentiment for the first time\u0026mdash;coincided with increased reporting of potential side effects and growing public debate about vaccine mandates. This crossover may represent a tipping point in public discourse, where the amplification of perceived risks began to outweigh the communication of benefits.\u003c/p\u003e\u003cp\u003eThis finding is consistent with research by Shaaban et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that social media platforms significantly contributed to the spread of vaccine concerns during the pandemic, with approximately 36% of individuals relying on social media for vaccine information. Our analysis further demonstrates that once negative narratives gain traction, they can persist and potentially influence attitudes toward vaccination more broadly, creating what Viswanath et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) describe as an \"infodemic\" that challenges public health communication efforts.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInstitutional Trust and Its Erosion\u003c/h3\u003e\n\u003cp\u003eTrust in institutions emerged as the most frequently discussed theme in our dataset, with initially strong positive sentiment that decreased considerably by May 2021 (from 45.5\u0026ndash;27.5%). This erosion of trust is particularly concerning, as it may have implications beyond COVID-19 vaccines. Anyiam-Osigwe et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlighted the significance of institutional trust in their analysis of post-COVID vaccination attitudes among healthcare workers, finding that decreased trust in authorities was associated with increased vaccine hesitancy for routine immunizations.\u003c/p\u003e\u003cp\u003eThe decline in institutional trust observed in our study may be explained through Institutional Trust Theory, which posits that trust is built through perceived competence, integrity, and benevolence of institutions. The rapid development and deployment of COVID-19 vaccines, combined with changing public health guidance and politicization of the pandemic response, may have challenged all three of these dimensions. This is reflected in our data showing that \"trust in institutions\" tweets, while exhibiting the highest positive sentiment (39.7%) among all themes, still demonstrated a concerning downward trend over time.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eDemographic Differences and Information Ecosystems\u003c/h2\u003e\u003cp\u003eOur analysis revealed significant demographic variations in vaccination sentiment, with users with larger followings (medium, large, and very large accounts) expressing higher levels of negative sentiment (15.3\u0026ndash;16.2%) compared to users with smaller followings (11.0-11.6%). This finding suggests that influential voices may be disproportionately amplifying negative vaccination narratives, creating what Viswanath et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) term \"echo chambers\" where skepticism is reinforced and amplified.\u003c/p\u003e\u003cp\u003eThe geographic differences in sentiment, with users from India showing higher levels of positive sentiment (19.3%) compared to users from the United States (with higher negative sentiment at 15.8%), align with findings by Shaaban et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who identified substantial cross-cultural variations in vaccine acceptance rates across 24 countries. These differences likely reflect varying experiences with vaccine rollout, different political contexts, and region-specific concerns, highlighting the need for culturally tailored approaches to vaccine communication.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eChildren's Vaccination: A Particular Concern\u003c/h2\u003e\u003cp\u003ePerhaps the most alarming finding in our study was the sharp increase in negative sentiment regarding childhood vaccination, which rose dramatically from 6.7% in December 2020 to 43.3% in April 2021. This shift coincided with expanded clinical trials of COVID-19 vaccines in pediatric populations and growing public debate about school vaccination requirements. The predominant concerns identified in our qualitative analysis\u0026mdash;safety and long-term effects (56.3%), necessity given lower COVID-19 risk in children (23.1%), and parental choice and autonomy (20.6%)\u0026mdash;mirror those found by Golder et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in their mixed-methods analysis of vaccination hesitancy during pregnancy, where safety concerns and the perceived rapid development of vaccines were primary barriers.\u003c/p\u003e\u003cp\u003eThis finding is particularly concerning because attitudes toward COVID-19 vaccination for children may influence perceptions of routine childhood immunizations. The Health Belief Model helps explain this phenomenon: as parents weigh the perceived benefits against perceived risks of vaccination, the heightened perception of risk associated with COVID-19 vaccines may spill over to other vaccines, potentially threatening the hard-won progress in global childhood immunization programs.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eMisinformation and Its Persistence\u003c/h2\u003e\u003cp\u003eOur investigation identified eight distinct categories of vaccine misinformation, with DNA alteration claims, government control narratives, and 5G connectivity claims being the most prevalent. While representing a relatively small portion of the overall discourse (approximately 2.7\u0026ndash;3.1% of monthly tweets), these misinformation narratives demonstrated remarkable persistence throughout the study period and generated substantial engagement\u0026mdash;an average of 20.3 retweets per misinformation tweet compared to 5.7 retweets for non-misinformation content.\u003c/p\u003e\u003cp\u003eThis disproportionate amplification of misinformation aligns with findings by Skafle et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who identified similar themes of misinformation in their systematic review, including concerns about DNA alterations, government surveillance/control, and inadequate testing. The persistence of these narratives throughout our study period suggests they had become established within vaccination discourse rather than representing temporary concerns, creating what Nyawa et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) describe as \"information bubbles\" that can be difficult to penetrate with factual information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003eImplications for Post-Pandemic Vaccination Efforts\u003c/h2\u003e\u003cp\u003eOur analysis of post-pandemic perspective tweets (0.08% of the dataset) revealed higher negative sentiment (26.2%) compared to the overall dataset (13.1%), suggesting that forward-looking discussions about vaccination contain more concerns than optimism. This finding, though based on a small subset of data, raises important questions about the potential long-term impact of the COVID-19 pandemic on vaccination attitudes more broadly.\u003c/p\u003e\u003cp\u003eThe patterns identified in our study\u0026mdash;declining trust in institutions, persistent misinformation, and increasing concerns about childhood vaccination\u0026mdash;could threaten routine immunization programs if left unaddressed. This concern is supported by recent research by Anyiam-Osigwe et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who found that the COVID-19 pandemic influenced healthcare workers' attitudes toward routine flu vaccination, leading to what they termed \"vaccine fatigue\" and heightened concerns about autonomy in vaccination decisions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eEngagement Patterns and Communication Strategies\u003c/h3\u003e\n\u003cp\u003eOur analysis of highly engaging content revealed that while negative content was less common, it generated more engagement when it did appear (average 18.456 interactions) compared to positive (12.877) or neutral (11.238) content. This engagement pattern aligns with the Social Amplification of Risk Framework, which suggests that negative information often spreads more widely and rapidly than positive information.\u003c/p\u003e\u003cp\u003eThis finding has important implications for public health communication strategies. While practical information about vaccine availability dominated the discourse, emotional content\u0026mdash;both positive vaccination experiences and negative side effect reports\u0026mdash;generated disproportionate engagement. This suggests that effective communication strategies should incorporate both factual information and emotional appeals, potentially leveraging positive personal stories to counterbalance the emotional impact of negative narratives.\u003c/p\u003e\n\u003ch3\u003eTheoretical Integration and Practical Implications\u003c/h3\u003e\n\u003cp\u003eThe integration of our empirical findings with established theoretical frameworks\u0026mdash;the Social Amplification of Risk Framework, the Health Belief Model, and Institutional Trust Theory\u0026mdash;reveals how negative perceptions about COVID-19 vaccines may transfer to routine immunization programs. The observed shift from initial optimism to increasing skepticism, particularly evident in the February 2021 crossover point where negative sentiment first exceeded positive, represents a potential inflection point in public attitudes toward vaccination more broadly.\u003c/p\u003e\u003cp\u003eFor public health practitioners, our findings highlight the need for tailored communication strategies that address specific concerns identified in this research. Particularly important is rebuilding institutional trust, addressing safety concerns about childhood vaccination, and developing effective approaches to counter persistent misinformation narratives. Geographic and demographic variations in sentiment suggest that one-size-fits-all approaches to vaccine communication may be ineffective, supporting the need for culturally and demographically targeted strategies.\u003c/p\u003e\u003cp\u003eThe role of influential social media users in amplifying negative narratives suggests that engaging digital opinion leaders in positive vaccination messaging could be an effective strategy. Additionally, the finding that emotional content generates disproportionate engagement indicates that combining factual information with compelling personal stories may be more effective than purely information-based approaches.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive examination of vaccination attitudes during a critical period of the COVID-19 pandemic, offering insights that may help anticipate and address challenges in the post-pandemic vaccination landscape. By analyzing nearly 77,000 tweets from December 2020 to May 2021, we have identified concerning trends that could affect vaccination acceptance beyond COVID-19, including declining positive sentiment, persistent misinformation narratives, influential negative voices, and growing concerns about childhood vaccination. The integration of our empirical findings with established theoretical frameworks\u0026mdash;the Social Amplification of Risk Framework, the Health Belief Model, and Institutional Trust Theory\u0026mdash;reveals how negative perceptions about COVID-19 vaccines may transfer to routine immunization programs. The observed shift from initial optimism to increasing skepticism, particularly evident in the February 2021 crossover point where negative sentiment first exceeded positive, represents a potential inflection point in public attitudes toward vaccination more broadly.\u003c/p\u003e\u003cp\u003eParticularly concerning is the sharp increase in negative sentiment regarding childhood vaccination in April 2021, which could have lasting implications for pediatric immunization programs. The prevalence of safety concerns and the amplification of misinformation through influential social media users present ongoing challenges for public health communication. Geographic and demographic variations in sentiment suggest that tailored, context-specific approaches will be necessary to rebuild vaccine confidence. These findings support United Nations Sustainable Development Goal 3 by providing actionable insights for public health officials working to ensure healthy lives and promote well-being for all ages. By understanding the specific concerns, misinformation narratives, and sentiment patterns identified in this research, health authorities can develop more effective strategies to rebuild public trust in vaccines and health institutions, both for ongoing COVID-19 vaccination efforts and for routine immunization programs in the post-pandemic era.\u003c/p\u003e\u003cp\u003eFuture research should continue to monitor vaccination attitudes as the world transitions from pandemic to post-pandemic status, particularly examining whether the patterns identified here persist, intensify, or resolve over time. Additional studies focusing specifically on attitudes toward childhood vaccination, the role of influential social media users in shaping discourse, and effective counter-misinformation strategies would further contribute to our understanding of this complex issue. Ultimately, the path to rebuilding and maintaining strong vaccine confidence in the post-COVID world requires acknowledging and addressing the legitimate concerns that have emerged during the pandemic, while simultaneously working to combat misinformation and restore trust in health authorities. This research provides a foundation for these efforts by identifying key challenges and opportunities in the evolving landscape of vaccination attitudes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Y.S.B., \u0026Ouml;.K. and E.A.; methodology, Y.S.B.; software and algorithm implementation, Y.S.B.; formal analysis, Y.S.B.; data collection, Y.S.B.and \u0026Ouml;.K.; writing\u0026mdash;original draft preparation, \u0026Ouml;.K., E.A.; writing\u0026mdash;review and editing, Y.S.B., \u0026Ouml;.K. and E.A.; supervision, \u0026Ouml;.K. and E.A. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe Twitter data used in this study cannot be publicly shared due to privacy concerns, Twitter's Terms of Service, and ethical considerations regarding user consent. The dataset contains personally identifiable information and potentially sensitive content from users who did not explicitly consent to participate in this research. Furthermore, Twitter's (now X's) Developer Agreement restricts the redistribution of tweet content in its original form.Instead, we provide the following:1.Detailed methodological information: Our manuscript contains comprehensive details about data collection methods, preprocessing steps, and analytical approaches to enable other researchers to replicate our study design.2.Aggregated statistics: Summary statistics, sentiment distribution, and thematic categorization results are available in the manuscript tables and figures.3.Coding framework: The sentiment analysis lexicon and thematic classification codebook used in this study can be made available upon reasonable request to the corresponding author, subject to a data usage agreement.4.Tweet IDs: Upon request and subject to appropriate data usage agreements, we can provide a list of tweet IDs that would allow qualified researchers to reconstruct the dataset using Twitter's API, in accordance with Twitter's Developer Policy. This approach is consistent with established practices in social media research that balance open science principles with privacy concerns.For inquiries regarding the dataset, please contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnyiam-Osigwe A, Katangwe-Chigamba T, Scott S et al (2024) A psychosocial critique of the consequences of the COVID-19 pandemic on UK care home staff attitudes to the flu vaccination: A qualitative longitudinal study. 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BMJ Global Health 5(10):e004206\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vaccine hesitancy, Social media analysis, COVID-19, Institutional trust, Childhood vaccination","lastPublishedDoi":"10.21203/rs.3.rs-6431026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6431026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic triggered unprecedented global vaccination campaigns while simultaneously fueling misinformation and vaccine hesitancy. This study analyzed 77,171 English-language tweets related to vaccination collected between December 2020 and May 2021 to understand how attitudes toward vaccination evolved during this critical period and may persist into the post-pandemic era. Employing a mixed-methods approach combining sentiment analysis, thematic classification, and demographic analysis, we identified concerning trends that could affect vaccination acceptance beyond COVID-19. Results revealed a significant decline in positive sentiment (18.3\u0026ndash;10.9%) and increase in negative sentiment (9.1\u0026ndash;14.6%) over the study period, with a critical inflection point occurring in February 2021. Trust in institutions emerged as the most frequently discussed theme, with initially strong positive sentiment that decreased considerably by May 2021. Childhood vaccination demonstrated a dramatic increase in negative sentiment, rising from 6.7\u0026ndash;43.3% by April 2021. Furthermore, users with larger follower counts were found to contribute more negative content, amplifying skepticism. The study identified eight key misinformation categories, including claims about DNA alteration, government control, and 5G connectivity. Interpreted through the Health Belief Model, Social Amplification of Risk Framework, and Institutional Trust Theory, the findings suggest that vaccine distrust may extend to routine immunizations. The results emphasize the urgency of tailored communication strategies to rebuild public trust in vaccines in the post-pandemic world.\u003c/p\u003e","manuscriptTitle":"Popular attitudes toward vaccination in the post-COVID-19 period: a social media analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 17:12:57","doi":"10.21203/rs.3.rs-6431026/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac95b673-bb23-49dd-b18a-eb7db409a14c","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52019155,"name":"Humanities/Health humanities"},{"id":52019156,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2025-11-10T12:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 17:12:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6431026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6431026","identity":"rs-6431026","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}