"Vaccines for pregnant women…?! Absurd" - Mapping maternal vaccination discourse and stance on social media over six months

Narratives, Information and Manifestations of Resistance to Persuasion in Online Discussions of HPV Vaccination

There are both theoretical accounts and empirical evidence for the fact that, in health communication, narratives (story telling) may have a persuasive advantage when compared with information (the provision of facts). The dominant explanation for this potential advantage is that narratives inhibit people's resistance to persuasion, particularly in the form of counterarguing. Evidence in this area to date has most often been gathered through lab or field experiments. In the current study we took a novel approach, gathering our data from naturally-occurring, non-experimental and organically evolving online interactions about vaccinations. We focus on five threads from the parenting forum Mumsnet Talk that centered on indecision about the HPV vaccination. Our analysis revealed that narratives and information were used by posters in similar quantities as a means of providing vaccination-related advice. We also found similar frequencies of direct engagement with both narratives and information. However, our findings showed that narratives resulted in a significantly higher proportion of posts exhibiting supportive engagement, whereas information resulted in posts exhibiting a significantly higher proportion of challenges, including counterarguing and other manifestations of posters' resistance to persuasion. The proportions of supportive versus challenging engagement also varied depending on the topic and vaccine stance of narratives. Notwithstanding contextual explanations for these patterns, our findings, based on this original approach of using naturalistic data, provide a novel kind of evidence for the potential of narratives to inhibit counterarguing in authentic health-related discourse.

Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study

BACKGROUND

The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post-COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post-COVID-19 condition on this demographic.

OBJECTIVE

With a social media analysis of the discourse surrounding the prevalence of post-COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post-COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post-COVID-19 condition and identify critical areas and future directions for researchers and policy makers.

METHODS

From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post-COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs.

RESULTS

We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post-COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences.

CONCLUSIONS

The perceptions described on Twitter by HCWs concerning the presence of the post-COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post-COVID-19 condition.

A Snapshot of COVID-19 Vaccine Discourse Related to Ethnic Minority Communities in the United Kingdom Between January and April 2022: Mixed Methods Analysis

BACKGROUND

Existing literature highlights the role of social media as a key source of information for the public during the COVID-19 pandemic and its influence on vaccination attempts. Yet there is little research exploring its role in the public discourse specifically among ethnic minority communities, who have the highest rates of vaccine hesitancy (delay or refusal of vaccination despite availability of services).

OBJECTIVE

This study aims to understand the discourse related to minority communities on social media platforms Twitter and YouTube.

METHODS

Social media data from the United Kingdom was extracted from Twitter and YouTube using the software Netlytics and YouTube Data Tools to provide a "snapshot" of the discourse between January and April 2022. A mixed method approach was used where qualitative data were contextualized into codes. Network analysis was applied to provide insight into the most frequent and weighted keywords and topics of conversations.

RESULTS

A total of 260 tweets and 156 comments from 4 YouTube videos were included in our analysis. Our data suggests that the most popular topics of conversation during the period sampled were related to communication strategies adopted during the booster vaccine rollout. These were noted to be divisive in nature and linked to wider conversations around racism and historical mistrust toward institutions.

CONCLUSIONS

Our study suggests a shift in narrative from concerns about the COVID-19 vaccine itself, toward the strategies used in vaccination implementation, in particular the targeting of ethnic minority groups through vaccination campaigns. The implications for public health communication during crisis management in a pandemic context include acknowledging wider experiences of discrimination when addressing ethnic minority communities.

Evaluating Messaging on Prenatal Health Behaviors Using Social Media Data: Systematic Review

BACKGROUND

Social media platforms are increasingly being used to disseminate messages about prenatal health. However, to date, we lack a systematic assessment of how to evaluate the impact of official prenatal health messaging and campaigns using social media data.

OBJECTIVE

This study aims to review both the published and gray literature on how official prenatal health messaging and campaigns have been evaluated to date in terms of impact, acceptability, effectiveness, and unintended consequences, using social media data.

METHODS

A total of 6 electronic databases were searched and supplemented with the hand-searching of reference lists. Both published and gray literature were eligible for review. Data were analyzed using content analysis for descriptive data and a thematic synthesis approach to summarize qualitative evidence. A quality appraisal tool, designed especially for use with social media data, was used to assess the quality of the included articles.

RESULTS

A total of 11 studies were eligible for the review. The results showed that the most common prenatal health behavior targeted was alcohol consumption, and Facebook was the most commonly used source of social media data. The majority (n=6) of articles used social media data for descriptive purposes only. The results also showed that there was a lack of evaluation of the effectiveness, acceptability, and unintended consequences of the prenatal health message or campaign.

CONCLUSIONS

Social media is a widely used and potentially valuable resource for communicating and evaluating prenatal health messaging. However, this review suggests that there is a need to develop and adopt sound methodology on how to evaluate prenatal health messaging using social media data, for the benefit of future research and to inform public health practice.

A scoping review of the reasons for and approaches to non-uptake of pertussis and influenza vaccinations in pregnant women in the United Kingdom and Ireland

BACKGROUND

Pertussis and influenza cause significant morbidity and mortality in pregnancy and the neonatal period. Maternal vaccination in pregnancy would reduce harm, but low vaccine uptake is a concern. This scoping review aimed to understand the reasons for, and approaches, to non-uptake of pertussis and influenza vaccinations in pregnant women in the UK and Ireland.

METHODS

The inclusion criteria of this scoping review consist of pregnant women who avail of pertussis and influenza vaccines in the UK and Ireland. MEDLINE, EMBASE, Web of Science and CINAHL databases were searched in June 2021 and updated in October 2022. Searches were limited to English language reports published after 2011. We followed the Joanna Briggs Institute guidance on scoping reviews. Data were extracted and charted.

RESULTS

Five themes emerged from the literature. Acceptability, as well as organisational and awareness issues, were overarching themes regarding reasons for and approaches to non-uptake of the vaccines respectively. Other themes included healthcare professional factors, information interpretation and pregnancy-related factors.

CONCLUSIONS

Women need clear, comprehensible information, ideally provided by their healthcare professionals, in a way that is meaningful and addresses their circumstances and risk perceptions. This research will serve as a base for future work that aims behaviour science interventions at the wider pregnant population as well as the target groups that have been identified in this review.

Online on the frontline: A longitudinal social media analysis of UK healthcare workers' attitudes to COVID-19 vaccines using the 5C framework

This paper explores vaccine hesitancy among healthcare workers (HCWs) in the UK, where different COVID-19 vaccines were being rolled out through a national vaccination campaign from 2020 to 2022, consisting of a first and second dose programme. Through a mixed-method approach using qualitative discourse analysis and network analysis of Twitter data, we assessed HCW perceptions and views about the administration and delivery of COVID-19 vaccines in the United Kingdom (UK). We were also interested in exploring HCWs' personal experiences and attitudes towards taking COVID-19 vaccines themselves. We drew upon sociology, ethics, communication studies and used research methods concentrating on social media and media analysis. By employing the '5C framework' of 'confidence, complacency, constraints, calculation, and collective responsibility' we evaluated a longitudinal selection of tweets to capture relevant factors driving vaccination views and behaviours among HCWs. We found differing positions expressed about COVID-19 vaccines and policy during the first dose compared with the second, through a drop in confidence compounded by supply and access issues, as well the news of a vaccine mandate for HCWs by the UK government in 2021. HCWs asked calculation questions to the community or brought forward competing pieces of information about vaccine policy and guidelines. Constraint levels in access issues were noted, especially for those with work and caregiving responsibilities, and student nurses found they did not have equal vaccination access. HCWs also displayed collective responsibility on social platforms to both encourage vaccination and express concerns through the organisation of social action against vaccine mandates.

Frontline Health Care Workers' Mental Health and Well-Being During the First Year of the COVID-19 Pandemic: Analysis of Interviews and Social Media Data

BACKGROUND

The COVID-19 pandemic has shed light on fractures in health care systems worldwide and continues to have a significant impact, particularly in relation to the health care workforce. Frontline staff have been exposed to unprecedented strain, and delivering care during the pandemic has affected their safety, mental health, and well-being.

OBJECTIVE

This study aimed to explore the experiences of health care workers (HCWs) delivering care in the United Kingdom during the COVID-19 pandemic to understand their well-being needs, experiences, and strategies used to maintain well-being (at individual and organizational levels).

METHODS

We analyzed 94 telephone interviews with HCWs and 2000 tweets about HCWs' mental health during the first year of the COVID-19 pandemic.

RESULTS

The results were grouped under 6 themes: redeployment, clinical work, and sense of duty; well-being support and HCW's coping strategies; negative mental health effects; organizational support; social network and support; and public and government support.

CONCLUSIONS

These findings demonstrate the need for open conversations, where staff's well-being needs and the strategies they adopted can be shared and encouraged, rather than implementing top-down psychological interventions alone. At the macro level, the findings also highlighted the impact on HCW's well-being of public and government support as well as the need to ensure protection through personal protective equipment, testing, and vaccines for frontline workers.

The Influence of Social Media and Institutional Trust on Vaccine Hesitancy in France: Examining Direct and Mediating Processes

Vaccine hesitancy (VH) remains an ongoing challenge in French society. This project explored how institutional trust and preference for information via social media (PISM) drive hesitancy. Across a large, nationally represented population, our findings show that PISM and trust are strongly correlated measures, with both independently predicting VH. Subsequent mediation tests show that social media operates as primarily an indirect contributor to VH through trust. Additional tests involving VH and non-VH typologies revealed that institutional trust consistently predicts greater general support for vaccines and reduced distrust in vaccination. Conversely, PISM directly drives vaccine distrust, with its impact on non-hesitancy fully mediated by institutional trust. Overall, these findings point to the relevance for researchers and public health deciders to address the nature by which people utilize social media information resources and how that interacts with levels of trust for national institutions.

A Descriptive Study of Maternal Vaccination Uptake, Attitudes, and Beliefs in Pregnancy Among Persons Delivering at an Urban Safety Net Hospital

Objective

To estimate uptake of influenza, tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap), and COVID-19 vaccines during pregnancy and describe vaccine attitudes and beliefs among predominantly racial and ethnic minority individuals delivering at a publicly funded hospital.

Methods

We collected survey and electronic medical record data for English-speaking postpartum individuals who delivered a live-born infant from July 7, 2022, through August 21, 2022, and agreed to participate in our study. The 58-item survey included questions about general vaccine attitudes and beliefs as well as vaccine-specific questions. We calculated rates of influenza, Tdap, and COVID-19 vaccinations and compared distributions of survey responses by number (no vaccines, one vaccine, or two or three of the recommended vaccines) and type of vaccines received during pregnancy.

Results

Of the 231 eligible individuals, 125 (54.1%) agreed to participate. Rates of influenza, Tdap, and COVID-19 vaccination were 18.4%, 48.0%, and 5.6% respectively. A total of 61 (48.8%) did not receive any recommended vaccines during pregnancy, 40 (32.0%) received one vaccine, and 24 (19.0%) received two or three vaccines. Approximately 66.1% of the no vaccine group, 81.6% of the one vaccine group, and 87.5% of the two or three vaccine group strongly agreed or agreed that they trusted the vaccine information provided by their obstetrician or midwife. While most (>69.2%) agreed that the vaccine-preventable diseases were dangerous for pregnant women, only 24.0%, 29.3%, and 40.3% agreed that they were worried about getting influenza, whooping cough, or COVID-19, respectively, while pregnant.

Discussion

Vaccine uptake in our population was low and may be due, in part, to low perceived susceptibility to vaccine-preventable diseases. Obstetricians and midwives were trusted sources of vaccine information, suggesting that enhanced communication strategies could be critical for addressing maternal vaccine hesitancy, particularly in communities of color justifiably affected by medical mistrust.

Exploring the vaccine conversation on TikTok in Italy: beyond classic vaccine stances

TikTok, a social media platform for creating and sharing short videos, has seen a surge in popularity during the COVID-19 pandemic. To analyse the Italian vaccine conversation on TikTok, we downloaded a sample of videos with a high play count (Top Videos), identified through an unofficial Application Programming Interface (consistent with TikTok's Terms of Service), and collected public videos from vaccine sceptic users through snowball sampling (Vaccine Sceptics' videos). The videos were analysed using qualitative and quantitative methods, in terms of vaccine stance, tone of voice, topic, conformity with TikTok style, and other characteristics. The final datasets consisted of 754 Top Videos (by 510 single users) plus 180 Vaccine Sceptics' videos (by 29 single users), posted between January 2020 and March 2021. In 40.5% of the Top Videos the stance was promotional, 33.9% were indefinite-ironic, 11.3% were neutral, 9.7% were discouraging, and 3.1% were ambiguous (i.e. expressing an ambivalent stance towards vaccines); 43% of promotional videos were from healthcare professionals. More than 95% of the Vaccine Sceptic videos were discouraging. Multiple correspondence analysis showed that, compared to other stances, promotional videos were more frequently created by healthcare professionals and by females, and their most frequent topic was herd immunity. Discouraging videos were associated with a polemical tone of voice and their topics were conspiracy and freedom of choice. Our analysis shows that Italian vaccine-sceptic users on TikTok are limited in number and vocality, and the large proportion of videos with an indefinite-ironic stance might imply that the incidence of affective polarisation could be lower on TikTok, compared to other social media, in the Italian context. Safety is the most frequent concern of users, and we recorded an interesting presence of healthcare professionals among the creators. TikTok should be considered as a medium for vaccine communication and for vaccine promotion campaigns.

COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter

Introduction

This study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from social media, and can be utilized for various natural language processing tasks such as classification, question-answering, and chatbots. This paper aims to evaluate the performance of CT-BERT on different classification datasets and compare it with BERT-LARGE, its base model.

Methods

The study utilizes CT-BERT, which is pre-trained on a large corpus of COVID-19 related Twitter messages. The authors evaluated the performance of CT-BERT on five different classification datasets, including one in the target domain. The model's performance is compared to its base model, BERT-LARGE, to measure the marginal improvement. The authors also provide detailed information on the training process and the technical specifications of the model.

Results

The results indicate that CT-BERT outperforms BERT-LARGE with a marginal improvement of 10-30% on all five classification datasets. The largest improvements are observed in the target domain. The authors provide detailed performance metrics and discuss the significance of these results.

Discussion

The study demonstrates the potential of pre-trained transformer models, such as CT-BERT, for COVID-19 related natural language processing tasks. The results indicate that CT-BERT can improve the classification performance on COVID-19 related content, especially on social media. These findings have important implications for various applications, such as monitoring public sentiment and developing chatbots to provide COVID-19 related information. The study also highlights the importance of using domain-specific pre-trained models for specific natural language processing tasks. Overall, this work provides a valuable contribution to the development of COVID-19 related NLP models.

The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust

The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.

"Any idea how fast 'It's just a mask!' can turn into 'It's just a vaccine!'": From mask mandates to vaccine mandates during the COVID-19 pandemic

Protests starting in the summer of 2020, notedly in the US and UK, have brought together two constituencies: pre-existing anti-vaccine groups and newly formed oppositional COVID-19 groups. The oppositional COVID-19 groups vary in composition and nature, but the central focus is a disagreement about the seriousness and threat of COVID-19 and with the public health measures to control COVID-19. What unites many disparate interests is an aversion to mandates. The compulsion to undertake particular public health activities such as mask-wearing and vaccination is a complex topic of public attitudes and beliefs alongside public health goals and messaging. We aim to analyse social media discussions about facemask wearing and the adoption of potential vaccines for COVID-19. Using media monitoring software Meltwater^TM, we analyse English-language tweets for one year from 1st June 2020 until 1st June 2021. We pay particular attention to connections in conversations between key topics of concern regarding masks and vaccines across social media networks. We track where ideas and activist behaviours towards both health interventions have originated, have similarities, and how they have changed over time. Our aim is to provide an overview of the key trends and themes of discussion concerning attitudes to and adoption of health measures in the control of COVID-19 and how publics react when confronted with mandatory policies. We draw on an already extensive literature about mandatory vaccination policies to inform our assessment, from psychology and behavioural science to ethics, political theory, sociology, and public policy.

Healthcare workers' attitudes on mandates, incentives, and strategies to improve COVID-19 vaccine uptake: A mixed methods study

Healthcare workers are a trusted health information source and are uniquely positioned to reduce the burden of the COVID-19 pandemic. The purpose of this sequential exploratory mixed methods study was to understand attitudes of healthcare workers working in Massachusetts during the COVID-19 pandemic regarding strategies to improve COVID-19 vaccine utilization, including vaccine mandates and incentives. Fifty-two individuals completed one-on-one interviews between April 22^nd and September 7th, 2021. The survey was developed based on findings from the interviews; 209 individuals completed the online survey between February 17^th and March 23^rd, 2022. Both the interview and survey asked about attitudes toward COVID-19 vaccine and booster mandates, incentives, and strategies to improve vaccination rates. Most participants were female (79%-interview, 81%-survey), Caucasian (56%, 73%), and worked as physicians (37%, 34%) or nurses (10%, 18%). Overall, nuanced attitudes regarding vaccine and booster mandates were expressed; many supported mandates to protect their patients' health, others emphasized personal autonomy, while some were against mandates if job termination was the consequence of declining vaccines. Similarly, views regarding vaccine incentives differed; some considered incentives helpful, yet many viewed them as coercive. Strategies believed to be most effective to encourage vaccination included improving accessibility to vaccination sites, addressing misinformation, discussing vaccine safety, tailored community outreach via trusted messengers, and one-on-one conversations between patients and healthcare workers. Healthcare workers' experiences with strategies to improve utilization of COVID-19 vaccines and boosters have implications for public health policies. Generally, efforts to improve access and education were viewed more favorably than incentives and mandates.

The deployment of social media by political authorities and health experts to enhance public information during the COVID-19 pandemic

Social media have increasingly been used by political bodies and experts to disseminate health information to the public. However, we still know little about how the communication of these actors on social media is received by other users and how it reflects trends in public trust. We examined social media dynamics in the communication of information by major actors (n = 188) involved in COVID-19 online discussions in Switzerland. These actors are scientists (experts), policymakers (government officials, cantonal executives, and other parties), and representatives of mass media. We found little correlation between Twitter features (other users' engagement and negativity in other users' replies) and the level of public trust found in representative opinion surveys. We used topic modelling in combination with correspondence analysis, and including additional variables for actor types and the period of the public debate further enabled us to detect salient episodes related to the pandemic on social media. In particular, we found that differing roles were played by the (health) experts and political authorities in terms of both topics and influence on the specific timing of the pandemic. The results of this study provide helpful conclusions for communication among political authorities, health experts, and the public.

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

COVID-19 vaccination hesitancy in pregnant and breastfeeding women and strategies to increase vaccination compliance: a systematic review and meta-analysis

INTRODUCTION

Pregnant and breastfeeding women are at an increased risk of severe illness from COVID-19. Despite this, low vaccination coverages are reported in this population sub-group.

AREAS COVERED

The purpose of this study is to estimate the proportion of pregnant and breastfeeding women expressing hesitation to the COVID-19 vaccine worldwide. Forty-six studies were included in the meta-analysis and systematic review, selected from scientific articles available in the MEDLINE/PubMed, Google Scholar, and Scopus databases between January 1, 2020 and February 6, 2022. The vaccine hesitation rate among pregnant and breastfeeding women was 48.4% (95%CI= 43.4-53.4%). In a sub analysis by study period, it was 40.0% (95%CI=31.6-46.6%) considering surveys administered in 2020, 58.0% (95%CI=48.9-66.9%) considering surveys administered in the first semester of 2021, and 38.1% (95%CI=25.9-51.2%) considering surveys administered in the second semester of 2021. The main reasons for vaccine hesitation were lack of information about vaccination, opinion that the vaccine is unsafe, and fear of adverse events.

EXPERT OPINION

Available evidence in the literature has shown that fighting vaccine resistance is harsh and too slow as a process, considering the rapidity and unpredictability of a pandemic. Health education should be provided in order to improve the willingness of the community, especially for those with lower levels of education.

Media Data and Vaccine Hesitancy: Scoping Review

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

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

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

Association Between Influenza Vaccination During Pregnancy and Breastfeeding Duration

Background: Prenatal vaccination and breastfeeding are both health promotional behaviors. The benefits of breastfeeding are widely investigated and well established, as well as the effectivity and the safety of the influenza vaccination. This study aimed to investigate the association between the prenatal health behavior of influenza vaccination and breastfeeding duration for at least 3 months in the United States. Methods: A secondary data analysis was conducted using 2016-2019 Pregnancy Risk Assessment and Monitoring System data consisting of 83,976 postpartum women. Results: Overall, 68.3% (n = 57,269) breastfed for at least 3 months and 56.6% (n = 48,834) received the influenza vaccine during pregnancy. Women who did not receive the influenza vaccine during pregnancy were significantly less likely to breastfeed compared with women who received the vaccine during pregnancy (64.5% versus 71.1%; p < 0.001). Significant covariates associated with ≥3-month breastfeeding duration included maternal sociodemographic factors, infant factors, maternal health factors, and prenatal health behaviors. In the multivariable logistic regression analyses, the odds of breastfeeding at least 3 months were significantly lower in women who did not receive the influenza vaccine during pregnancy compared with those who were vaccinated (odds ratio, 0.92; 95% confidence interval, 0.88-0.98; p = 0.005). Discussion: Findings from this large, nationally representative sample of U.S. mothers indicate an association between the health-promotional behaviors of prenatal influenza vaccination and breastfeeding duration postpartum. Recommendations and education by health care providers should include a focus on maternal health behaviors to support positive maternal-infant health outcomes including reduced risk of infection and extended breastfeeding duration.

Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms

BACKGROUND

Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns.

METHODS

A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States.

RESULTS

The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates.

CONCLUSIONS

Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

Dynamic assessment of the COVID-19 vaccine acceptance leveraging social media data

Vaccination is the most effective way to provide long-lasting immunity against viral infection; thus, rapid assessment of vaccine acceptance is a pressing challenge for health authorities. Prior studies have applied survey techniques to investigate vaccine acceptance, but these may be slow and expensive. This study investigates 29 million vaccine-related tweets from August 8, 2020 to April 19, 2021 and proposes a social media-based approach that derives a vaccine acceptance index (VAI) to quantify Twitter users’ opinions on COVID-19 vaccination. This index is calculated based on opinion classifications identified with the aid of natural language processing techniques and provides a quantitative metric to indicate the level of vaccine acceptance across different geographic scales in the U.S. The VAI is easily calculated from the number of positive and negative Tweets posted by a specific users and groups of users, it can be compiled for regions such a counties or states to provide geospatial information, and it can be tracked over time to assess changes in vaccine acceptance as related to trends in the media and politics. At the national level, it showed that the VAI moved from negative to positive in 2020 and maintained steady after January 2021. Through exploratory analysis of state- and county-level data, reliable assessments of VAI against subsequent vaccination rates could be made for counties with at least 30 users. The paper discusses information characteristics that enable consistent estimation of VAI. The findings support the use of social media to understand opinions and to offer a timely and cost-effective way to assess vaccine acceptance.

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy

A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.

Re-ordering connections: UK healthcare workers' experiences of emotion management during the COVID-19 pandemic

This paper examines the impact of disruptions to the organisation and delivery of healthcare services and efforts to re-order care through emotion management during the COVID-19 pandemic in the UK. Framing care as an affective practice, studying healthcare workers' (HCWs) experiences enables better understanding of how interactions between staff, patients and families changed as a result of the pandemic. Using a rapid qualitative research methodology, we conducted interviews with frontline HCWs in two London hospitals during the peak of the first wave of the pandemic and sourced public accounts of HCWs' experiences of the pandemic from social media (YouTube and Twitter). We conducted framework analysis to identify key factors disrupting caring interactions. Fear of infection and the barriers of physical distancing acted to separate staff from patients and families, requiring new affective practices to repair connections. Witnessing suffering was distressing for staff, and providing a 'good death' for patients and communicating care to families was harder. In addition to caring for patients and families, HCWs cared for each other. Infection control measures were important for limiting the spread of COVID-19 but disrupted connections that were integral to care, generating new work to re-order interactions.

Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse

Background

Social media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text.

Objective

The aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy.

Methods

A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators.

Results

We found the accuracy of the machine learning techniques to be 81.8% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3%, 77.9%, and 77.5%.

Conclusions

This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions.

Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts

BACKGROUND

Measuring public response during COVID-19 is an important way of ensuring the suitability and effectiveness of epidemic response efforts. An analysis of social media provides an approximation of public sentiment during an emergency like the current pandemic. The measures introduced across the globe to help curtail the spread of the coronavirus have led to the development of a situation labeled as a "perfect storm," triggering a wave of domestic violence. As people use social media to communicate their experiences, analyzing public discourse and sentiment on social platforms offers a way to understand concerns and issues related to domestic violence during the COVID-19 pandemic.

OBJECTIVE

This study was based on an analysis of public discourse and sentiment related to domestic violence during the stay-at-home periods of the COVID-19 pandemic in Australia in 2020. It aimed to understand the more personal self-reported experiences, emotions, and reactions toward domestic violence that were not always classified or collected by official public bodies during the pandemic.

METHODS

We searched social media and news posts in Australia using key terms related to domestic violence and COVID-19 during 2020 via digital analytics tools to determine sentiments related to domestic violence during this period.

RESULTS

The study showed that the use of sentiment and discourse analysis to assess social media data is useful in measuring the public expression of feelings and sharing of resources in relation to the otherwise personal experience of domestic violence. There were a total of 63,800 posts across social media and news media. Within these posts, our analysis found that domestic violence was mentioned an average of 179 times a day. There were 30,100 tweets, 31,700 news reports, 1500 blog posts, 548 forum posts, and 7 comments (posted on news and blog websites). Negative or neutral sentiment centered on the sharp rise in domestic violence during different lockdown periods of the 2020 pandemic, and neutral and positive sentiments centered on praise for efforts that raised awareness of domestic violence as well as the positive actions of domestic violence charities and support groups in their campaigns. There were calls for a positive and proactive handling (rather than a mishandling) of the pandemic, and results indicated a high level of public discontent related to the rising rates of domestic violence and the lack of services during the pandemic.

CONCLUSIONS

This study provided a timely understanding of public sentiment related to domestic violence during the COVID-19 lockdown periods in Australia using social media analysis. Social media represents an important avenue for the dissemination of information; posts can be widely dispersed and easily accessed by a range of different communities who are often difficult to reach. An improved understanding of these issues is important for future policy direction. Heightened awareness of this could help agencies tailor and target messaging to maximize impact.

Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis

Objective

To identify themes and temporal trends in the sentiment of COVID-19 vaccine-related tweets and to explore variations in sentiment at world national and United States state levels.

Methods

We collected English-language tweets related to COVID-19 vaccines posted between November 1, 2020, and January 31, 2021. We applied the Valence Aware Dictionary and sEntiment Reasoner tool to calculate the compound score to determine whether the sentiment mentioned in each tweet was positive (compound ≥ 0.05), neutral (-0.05 < compound < 0.05), or negative (compound ≤ -0.05). We applied the latent Dirichlet allocation analysis to extract main topics for tweets with positive and negative sentiment. Then we performed a temporal analysis to identify time trends and a geographic analysis to explore sentiment differences in tweets posted in different locations.

Results

Out of a total of 2,678,372 COVID-19 vaccine-related tweets, tweets with positive, neutral, and negative sentiments were 42.8%, 26.9%, and 30.3%, respectively. We identified five themes for positive sentiment tweets (trial results, administration, life, information, and efficacy) and five themes for negative sentiment tweets (trial results, conspiracy, trust, effectiveness, and administration). On November 9, 2020, the sentiment score increased significantly (score = 0.234, p = 0.001), then slowly decreased to a neutral sentiment in late December and was maintained until the end of January. At the country level, tweets posted in Brazil had the lowest sentiment score of −0.002, while tweets posted in the United Arab Emirates had the highest sentiment score of 0.162. The overall average sentiment score for the United States was 0.089, with Washington, DC having the highest sentiment score of 0.144 and Wyoming having the lowest sentiment score of 0.036.

Conclusions

Public sentiment on COVID-19 vaccines varied significantly over time and geography. Sentiment analysis can provide timely insights into public sentiment toward the COVID-19 vaccine and guide public health policymakers in designing locally tailored vaccine education programs.

Advances of Sentiment Analysis Applications in Obstetrics/Gynecology and Midwifery

BACKGROUND

Sentiment analysis, which is also referred to as 'opinion mining' or 'emotion AI', processes natural language, analyzes text and employs computational linguistics, and biometrics to identify and analyze emotions and subjective information. Sentiment analysis is mostly applied in domains such as marketing and customer service but also in clinical medicine. Clinical medicine- related sentiment analysis has advanced recently, as more and more researchers are performing studies with the help of this valuable technique, having noticed its ability to contribute in the field.

OBJECTIVE

The aim of this review was to present important facts about sentimental analysis described in deposited articles in on-line databases and the relevant articles critically appraised and a narrative synthesis conducted.

METHODS

A systematic search of four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect) was performed. This review considered only quantitative, primary studies in English language, without geographical limitations, published from 2006-2021 and relevant to the objective. Searching terms were 'Sentiment analysis' AND 'Obstetrics' OR 'pregnancy', OR 'COVID' OR 'Perinatal distress' OR 'postpartum period' OR 'fetal' OR 'breast feeding' OR 'cervical'.

RESULTS AND DISCUSSION

Relevant articles were critically appraised and a narrative synthesis was conducted. As a large number of studies, illustrates the use of sentiment analysis in the domain of clinical medicine, it is proved to be extremely helpful, assisting in the investigation of some highly important and even previously unexplored issues.

CONCLUSION

Since pregnant women express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. Given the vast knowledge sentiment analysis has already offered, further studies employing this technique are expected in the future.

Maternal Vaccination in Uganda: Exploring Pregnant Women, Community Leaders and Healthcare Workers' Perceptions

Background: We investigated pregnant women, community leaders, healthcare workers (HCWs) and programme managers' perceptions of maternal vaccination in Kampala, Uganda. Methods: We conducted focus group discussions, key informant interviews and in-depth discussions with HCWs (3), community leaders (3), pregnant women (8) and programme managers (10) between November 2019 and October 2020. Data were analysed thematically. Results: Pregnant women, community leaders and some HCWs had limited maternal immunisation knowledge. There was confusion over what constitutes a vaccine. Pregnant women may not receive vaccines because of mistrust of government; use of expired vaccines; reliance on traditional medicine; religious beliefs; fear of side effects; HCWs attitudes; and logistical issues. The key facilitators of maternal vaccination were a desire to prevent diseases, positive influences from HCWs and information about vaccine side effects. Community leaders and some pregnant women highlighted that pregnant women do not make decisions about maternal vaccination independently and are influenced by different individuals, including other pregnant women, older people, partners, relatives (parents), community leaders, HCWs and the government. Conclusions: Our results indicate that public health messaging should target all community members, including partners and parents of pregnant women as well as HCWs, to improve knowledge of and confidence in maternal vaccines.

Vaccination as a Control Tool in Bovine Tuberculosis: Social Media Monitoring to Assess Public Response to Government Policy Development and Implementation

Vaccine hesitancy does not only concern human vaccines but incorporates One Health policies also; including vaccination of cattle and badgers as part of the government's bovine tuberculosis eradication strategy for England. Both digital and social media can propagate healthcare misinformation and thus affect vaccine policy support. The use of social media monitoring to understand real-time public perceptions of One Health policies is crucial to identify misinformation and address public concerns appropriately to achieve successful policy implementation. Digital and social media data surrounding two government announcements regarding the bovine tuberculosis eradication strategy for England were collected and screened using the Meltwater media monitoring platform. Communication patterns were studied using InfraNodus. Twitter analysis was conducted to identify key influencers, public engagement, and trending communications. Online social media activity increased rapidly after each announcement. Initially, badger culling took primary public concern and major influencers were identified. Cattle vaccination dominated discussion after the second announcement, with public perception being influenced by increased online activity from news sites, animal welfare charities, governmental bodies, and medical professionals. The greatest ambiguity towards the strategy was detected within farming communities, with the main disparity existing between cattle vaccination and badger culling opinions. Social media monitoring has potential use in surveying public perception of government policy, both prior to, and after implementation to identify and address areas of miscommunication and misinformation to improve public support for One Health policies.

Digital tools, multidisciplinarity and innovation for communicating vaccine safety in the COVID-19 era

The research on the anti-SARS-CoV-2 vaccines and their commercialization is an opportunity to test and consolidate the current knowledge along with the diverse available tools related to vaccine communication.

In this paper we explore the value of a multidisciplinary approach in this field: vaccine communication activities can be combined with insights from data science, risk communication, behavior change, design thinking and digital marketing. Methodologies derived from innovation and human-centered design can contribute to the development and testing of vaccine promotion campaigns, speeding up processes and streamlining the production of evidence in this area. The multiple activities involving the anti-SARS-CoV-2 vaccine could be the fertile ground in which to develop, test and refine effective frameworks to transfer data acquired through listening strategies into effective vaccine promotion campaigns. The World Health Organization’s Vaccine Safety Net is an interesting example of trusted information sources that aim toward using digital tools and innovative methodologies for communicating science-based information on vaccine safety at the global level.

The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.