Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15

"Can Anyone Tell Me…". Online Health Communities in Diabetes Self-Management in Poland and Italy

This paper contributes to the debate about the role of Online Health Communities (OHCs) in the healthcare system by concentrating on the kind of information sought and shared by their members. The paper focuses on OHCs for diabetes and discusses the main findings of a qualitative study conducted in Italy and Poland. The Uses and Gratifications approach informed the study, while content analysis was used to perform the analysis. The findings show that OHCs' role goes beyond information and emotional support, which relies on expertise by experience. Indeed, the lack of basic knowledge constituting the essential diabetes literacy for self-management was partially compensated by peer exchange in the OHCs. This raises at least two problems: quality and reliability of the information shared online, and consequences in terms of the equity that a healthcare system provides.

HPV Vaccination Rates of 7th Grade Students After a Strong Recommending Statement from the School Nurse

The Human Papillomavirus (HPV) vaccine can prevent 90% of cancers caused by HPV. Health care provider recommendations affect vaccine uptake, yet there are a lack of studies examining the impact of the school nurse (SN) in vaccine recommendations. The purpose of this study was to evaluate the impact of adding a SN HPV recommendation to the standard vaccination letter sent to parents/guardians. The rate of vaccination between the intervention and control schools was not statistically significant (Estimate (Std. Error) = -0.3066 (0.2151), p = 0.154). After controlling for age, sex, race, insurance type, and medical practice type, there was no significant difference in the likelihood to receive the HPV vaccine (OR = 1.53, 95% CI: 0.563-4.19 in 2018; OR = 1.34, 95% CI: 0.124-14.54 in 2019. Further work is needed to clarify how school nurses can better promote HPV vaccine, and which adolescent demographic groups (e.g., race, insurance type, provider type) face barriers to HPV vaccine uptake.

Trust in information sources as a moderator of the impact of COVID-19 anxiety and exposure to information on conspiracy thinking and misinformation beliefs: a multilevel study

This study investigates the intricate relationship between exposure to information sources, trust in these sources, conspiracy and misinformation beliefs, and COVID-19 anxiety among 509 Omani citizens aged 11 to 50, representing 11 governorates. Employing structural equation modeling, we not only examine these associations but also explore how trust and COVID-19 anxiety act as moderating variables in this context. Additionally, we delve into demographic factors such as age group, educational level, gender, and place of residence (governorate) to discern potential variations.Our findings reveal that trust in health experts is inversely related to belief in conspiracy theories, while trust in health experts negatively correlates with exposure to conspiracy and misinformation. Intriguingly, trust in health experts exhibits divergent effects across governorates: it diminishes conspiracy and misinformation beliefs in some regions but not in others. Exposure to personal contacts and digital media, on the other hand, is associated with heightened beliefs in misinformation and conspiracy theories, respectively, in select governorates. These distinctions may be attributed to proximity to Muscat, the capital city of Oman, where various media outlets and policy-making institutions are situated. Furthermore, lower educational attainment is linked to greater belief in conspiracy and misinformation. Females reported higher levels of conspiracy theory beliefs and COVID-19 anxiety while no significant differences were detected in misinformation beliefs.This study sheds light on the intricate dynamics of misinformation and conspiracy theories in the context of COVID-19 in Oman, highlighting the pivotal roles of trust and COVID-19 anxiety as moderating factors. These findings offer valuable insights into understanding and addressing the spread of misinformation and conspiracy theories during a public health crisis.

A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling

The proliferation of anti-vaccination arguments is a threat to the success of many immunization programmes. Effective rebuttal of contrarian arguments requires an approach that goes beyond addressing flaws in the arguments, by also considering the attitude roots-that is, the underlying psychological attributes driving a person's belief-of opposition to vaccines. Here, through a pre-registered systematic literature review of 152 scientific articles and thematic analysis of anti-vaccination arguments, we developed a hierarchical taxonomy that relates common arguments and themes to 11 attitude roots that explain why an individual might express opposition to vaccination. We further validated our taxonomy on coronavirus disease 2019 anti-vaccination misinformation, through a combination of human coding and machine learning using natural language processing algorithms. Overall, the taxonomy serves as a theoretical framework to link expressed opposition of vaccines to their underlying psychological processes. This enables future work to develop targeted rebuttals and other interventions that address the underlying motives of anti-vaccination arguments.

Human behavior in the time of COVID-19: Learning from big data

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups-using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

Vaccine development for bacterial pathogens: Advances, challenges and prospects

The advent and use of antimicrobials have played a key role in treating potentially life-threatening infectious diseases, improving health, and saving the lives of millions of people worldwide. However, the emergence of multidrug resistant (MDR) pathogens has been a significant health challenge that has compromised the ability to prevent and treat a wide range of infectious diseases that were once treatable. Vaccines offer potential as a promising alternative to fight against antimicrobial resistance (AMR) infectious diseases. Vaccine technologies include reverse vaccinology, structural biology methods, nucleic acid (DNA and mRNA) vaccines, generalised modules for membrane antigens, bioconjugates/glycoconjugates, nanomaterials and several other emerging technological advances that are offering a potential breakthrough in the development of efficient vaccines against pathogens. This review covers the opportunities and advancements in vaccine discovery and development targeting bacterial pathogens. We reflect on the impact of the already-developed vaccines targeting bacterial pathogens and the potential of those currently under different stages of preclinical and clinical trials. More importantly, we critically and comprehensively analyse the challenges while highlighting the key indices for future vaccine prospects. Finally, the issues and concerns of AMR for low-income countries (sub-Saharan Africa) and the challenges with vaccine integration, discovery and development in this region are critically evaluated.

Attitudes of Swedish Language Twitter Users Toward COVID-19 Vaccination: Exploratory Qualitative Study

Background

Social media have played an important role in shaping COVID-19 vaccine choices during the pandemic. Understanding people’s attitudes toward the vaccine as expressed on social media can help address the concerns of vaccine-hesitant individuals.

Objective

The aim of this study was to understand the attitudes of Swedish-speaking Twitter users toward COVID-19 vaccines.

Methods

This was an exploratory qualitative study that used a social media–listening approach. Between January and March 2022, a total of 2877 publicly available tweets in Swedish were systematically extracted from Twitter. A deductive thematic analysis was conducted using the World Health Organization’s 3C model (confidence, complacency, and convenience).

Results

Confidence in the safety and effectiveness of the COVID-19 vaccine appeared to be a major concern expressed on Twitter. Unclear governmental strategies in managing the pandemic in Sweden and the belief in conspiracy theories have further influenced negative attitudes toward vaccines. Complacency—the perceived risk of COVID-19 was low and booster vaccination was unnecessary; many expressed trust in natural immunity. Convenience—in terms of accessing the right information and the vaccine—highlighted a knowledge gap about the benefits and necessity of the vaccine, as well as complaints about the quality of vaccination services.

Conclusions

Swedish-speaking Twitter users in this study had negative attitudes toward COVID-19 vaccines, particularly booster vaccines. We identified attitudes toward vaccines and misinformation, indicating that social media monitoring can help policy makers respond by developing proactive health communication interventions.

Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations

High uptake of vaccinations is essential in fighting infectious diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Social media play a crucial role in propagating misinformation about vaccination, including through conspiracy theories and can negatively impact trust in vaccination. Users typically engage with multiple social media platforms; however, little is known about the role and content of cross-platform use in spreading vaccination-related information. This study examined the content and dynamics of YouTube videos shared in vaccine-related tweets posted to COVID-19 conversations before the COVID-19 vaccine rollout. We screened approximately 144 million tweets posted to COVID-19 conversations and identified 930,539 unique tweets in English that discussed vaccinations posted between 1 February and 23 June 2020. We then identified links to 2,097 unique YouTube videos that were tweeted. Analysis of the video transcripts using Latent Dirichlet Allocation topic modeling and independent coders indicate the dominance of conspiracy theories. Following the World Health Organization's declaration of the COVID-19 outbreak as a public health emergency of international concern, anti-vaccination frames rapidly transitioned from claiming that vaccines cause autism to pandemic conspiracy theories, often featuring Bill Gates. Content analysis of the 20 most tweeted videos revealed that the majority (n = 15) opposed vaccination and included conspiracy theories. Their spread on Twitter was consistent with spamming and coordinated efforts. These findings show the role of cross-platform sharing of YouTube videos over Twitter as a strategy to propagate primarily anti-vaccination messages. Future policies and interventions should consider how to counteract misinformation spread via such cross-platform activities.

Evolving perceptions of COVID-19 vaccines among remote Alaskan communities

Given the dynamic nature of the ongoing pandemic, public knowledge and perceptions about COVID-19 are evolving. Limited transportation options, inconsistent healthcare resources, and lack of water and sanitation infrastructure in many remote Alaskan communities located off the road system have contributed to the experience of the COVID-19 pandemic in these areas. We used longitudinal surveys to evaluate remote Alaskan residents' early vaccine acceptance, vaccine uptake and motivations, risk perceptions regarding COVID-19 vaccines, and likelihood of getting a booster. Slightly over half of respondents showed early vaccine acceptance (November/December 2020), with the highest rate among those over the age of 65 years. However, by March 2021, 80.7% of participants reported receiving the COVID-19 vaccine or planning to get one. Of the unvaccinated, reasons for not getting a vaccine included concerns about side effects and not trusting the vaccine. By September 2021, 88.5% of people had received two doses of a COVID-19 vaccine and 79.7% said they would get the booster (third dose) when it became available. There were misconceptions about vaccine recommendations for pregnant women and effects on fertility and DNA. Although initial vaccine concerns may have subsided, the booster rollout and forthcoming vaccines for youth under 12 years of age present new hurdles for vaccine communication efforts.

Anti-Vaccine Discourse on Social Media: An Exploratory Audit of Negative Tweets about Vaccines and Their Posters

As the anti-vaccination movement is spreading around the world, this paper addresses the ever more urgent need for health professionals, communicators and policy-makers to grasp the nature of vaccine mis/disinformation on social media. A one-by-one coding of 4511 vaccine-related tweets posted from the UK in 2019 resulted in 334 anti-vaccine tweets. Our analysis shows that (a) anti-vaccine tweeters are quite active and widely networked users on their own; (b) anti-vaccine messages tend to focus on the "harmful" nature of vaccination, based mostly on personal experience, values and beliefs rather than hard facts; (c) anonymity does not make a difference to the types of posted anti-vaccine content, but does so in terms of the volume of such content. Communication initiatives against anti-vaccination should (a) work closely with technological platforms to tackle anonymous anti-vaccine tweets; (b) focus efforts on mis/disinformation in three major arears (in order of importance): the medical nature of vaccines, the belief that vaccination is a tool of manipulation and control for money and power, and the "freedom of health choice" discourse against mandatory vaccination; and (c) go beyond common factual measures-such as detecting, labelling or removing fake news-to address emotions induced by personal memories, values and beliefs.

Sentiment analysis of Indian Tweets about Covid-19 vaccines

People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against ‘Covid19vaccine’ and ‘Coronavirusvaccine’ hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words ‘Delhi heart’, ‘Lung institute’, ‘Gift’, ‘Unite2fightcorona’, and ‘Covid-19 Vaccine’ are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards ‘Covid-19 vaccines’, gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine.

Biases in using social media data for public health surveillance: A scoping review

OBJECTIVES

A landscape scan of the methods that are used to either assess or mitigate biases when using social media data for public health surveillance, through a scoping review.

MATERIALS AND METHODS

Following best practices, we searched two literature databases (i.e., PubMed and Web of Science) and covered literature published up to July 2021. Through two rounds of screening (i.e., title/abstract screening, and then full-text screening), we extracted study objectives, analysis methods, and the methods used to assess or address the different biases from the eligible articles.

RESULTS

We identified a total of 2,856 articles from the two databases. After the screening processes, we extracted and synthesized 20 studies that either assessed or mitigated biases when leveraging social media data for public health surveillance. Researchers have tried to assess or address several different types of biases such as demographic bias, keyword bias, and platform bias. In particular, we found 11 studies that tried to measure the reliability of the research findings from social media data by comparing them with other data sources.

DISCUSSION AND CONCLUSION

We synthesized the types of biases and the methods used to assess or address the biases in studies that use social media data for public health surveillance. We found very few studies, despite the large number of publications using social media data, considered the various bias issues that are present from data collection to analysis methods. Overlooking bias can distort the study results and lead to unintended consequences, especially in the field of public health surveillance. These research gaps warrant further investigations more systematically. Strategies from other fields for addressing biases can be introduced for future public health surveillance systems that use social media data.

Attitudes toward COVID-19 Vaccination on Social Media: A Cross-Platform Analysis

During the COVID-19 pandemic, social media content analysis allowed for tracking attitudes toward newly introduced vaccines. However, current evidence is limited to single social media platforms. Our objective was to compare arguments used by anti-vaxxers in the context of COVID-19 vaccines across Facebook, Twitter, Instagram, and TikTok. We obtained the data set of 53,671 comments regarding COVID-19 vaccination published between August 2021 and February 2022. After that, we established categories of anti-vaccine content, manually classified comments, and compared the frequency of occurrence of the categories between social media platforms. We found that anti-vaxxers on social media use 14 categories of arguments against COVID-19 vaccines. The frequency of these categories varies across different social media platforms. The anti-vaxxers' activity on Facebook and Twitter is similar, focusing mainly on distrust of government and allegations regarding vaccination safety and effectiveness. Anti-vaxxers on TikTok mainly focus on personal freedom, while Instagram users encouraging vaccination often face criticism suggesting that vaccination is a private matter that should not be shared. Due to the differences in vaccine sentiment among users of different social media platforms, future research and educational campaigns should consider these distinctions, focusing more on the platforms popular among adolescents (i.e., Instagram and TikTok).

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

BACKGROUND

A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness.

OBJECTIVE

This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods.

METHODS

We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both.

RESULTS

Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown.

CONCLUSIONS

There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.

COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit

BACKGROUND

Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline.

METHODS

We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits.

RESULTS

There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal).

CONCLUSIONS

Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users were included in the study population. Multinomial logistic regression and counterfactual analysis were conducted. Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines, either pro-vaccine ( B = 0.40 , SE = 0.08 , P < 0.001 , OR = 1.49 ; 95 % CI = 1.26 -- 1.75 ) or anti-vaccine ( B = 0.52 , SE = 0.06 , P < 0.001 , OR = 1.69 ; 95 % CI = 1.49 -- 1.91 ). People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion ( B = - 0.18 , SE = 0.04 , P < 0.001 , OR = 0.84 ; 95 % CI = 0.77 -- 0.90 ). The United States public is most concerned about the safety, effectiveness, and political issues regarding vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. Conclusion Opinion on COVID-19 vaccine uptake varies across people of different characteristics.

The conspiracy of Covid-19 and 5G: Spatial analysis fallacies in the age of data democratization

In a context of mistrust in public health institutions and practices, anti-COVID/vaccination protests and the storming of Congress have illustrated that conspiracy theories are real and immanent threat to health and wellbeing, democracy, and public understanding of science. One manifestation of this is the suggested correlation of COVID-19 with 5G mobile technology. Throughout 2020, this alleged correlation was promoted and distributed widely on social media, often in the form of maps overlaying the distribution of COVID-19 cases with the instillation of 5G towers. These conspiracy theories are not fringe phenomena, and they form part of a growing repertoire for conspiracist activist groups with capacities for organised violence. In this paper, we outline how spatial data have been co-opted, and spatial correlations asserted by conspiracy theorists. We consider the basis of their claims of causal association with reference to three key areas of geographical explanation: (1) how social properties are constituted and how they exert complex causal forces, (2) the pitfalls of correlation with spatial and ecological data, and (3) the challenges of specifying and interpreting causal effects with spatial data. For each, we consider the unique theoretical and technical challenges involved in specifying meaningful correlation, and how their discarding facilitates conspiracist attribution. In doing so, we offer a basis both to interrogate conspiracists' uses and interpretation of data from elementary principles and offer some cautionary notes on the potential for their future misuse in an age of data democratization. Finally, this paper contributes to work on the basis of conspiracy theories in general, by asserting how - absent an appreciation of these key methodological principles - spatial health data may be especially prone to co-option by conspiracist groups.

Does the COVID-19 Vaccine Still Work That "Most of the Confirmed Cases Had Been Vaccinated"? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing

In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine's effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine's effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public's cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues.

Understanding the Temporal Effects on Tweetcussion of COVID-19 Vaccine

In the fight against COVID‐19, the Pfizer and BioNTech vaccine announcement marked a significant turning point. Analysing the topics discussed surrounding the announcement is critical to shed light on how people respond to the vaccination against COVID‐19. Specifically, since the COVID‐19 vaccine was developed at unprecedented speed, different segments of the public with a different understanding of the issues may react and respond differently. We analysed Twitter tweets to uncover the issues surrounding people's discussion of the vaccination against COVID‐19. Through the use of Latent Dirichlet Allocation (LDA), nine topics were identified pertaining to vaccine‐related tweets. We analysed the temporal differences in the nine topics, prior and after the official vaccine announcement.

Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

BACKGROUND

The onset of the COVID-19 pandemic and the consequent "infodemic" increased concerns about Twitter's role in advancing antivaccination messages, even before a vaccine became available to the public. New computational methods allow for analysis of cross-platform use by tracking links to websites shared over Twitter, which, in turn, can uncover some of the content and dynamics of information sources and agenda-setting processes. Such understanding can advance theory and efforts to reduce misinformation.

OBJECTIVE

Informed by agenda-setting theory, this study aimed to identify the content and temporal patterns of websites shared in vaccine-related tweets posted to COVID-19 conversations on Twitter between February and June 2020.

METHODS

We used triangulation of data analysis methods. Data mining consisted of the screening of around 5 million tweets posted to COVID-19 conversations to identify tweets that related to vaccination and including links to websites shared within these tweets. We further analyzed the content the 20 most-shared external websites using a mixed methods approach.

RESULTS

Of 841,896 vaccination-related tweets identified, 185,994 (22.1%) contained links to specific websites. A wide range of websites were shared, with the 20 most-tweeted websites constituting 14.5% (27,060/185,994) of the shared websites and typically being shared for only 2 to 3 days. Traditional media constituted the majority of these 20 websites, along with other social media and governmental sources. We identified markers of inauthentic propagation for some of these links.

CONCLUSIONS

The topic of vaccination was prevalent in tweets about COVID-19 early in the pandemic. Sharing websites was a common communication strategy, and its "bursty" pattern and inauthentic propagation strategies pose challenges for health promotion efforts. Future studies should consider cross-platform use in dissemination of health information and in counteracting misinformation.

COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies

BACKGROUND

False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data.

OBJECTIVE

In this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning.

METHODS

We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared.

RESULTS

We gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword-centered data collection with more than 1.8 million tweets, and (2) a historical account-level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility.

CONCLUSIONS

The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy.

The effects of misinformation on COVID-19 vaccine hesitancy in Bangladesh

Purpose

The main purpose of this study is to assess the prevalence of COVID-19 vaccine hesitancy among the general population in Bangladesh and the role of misinformation in this process.

Design/methodology/approach

An online survey was conducted to assess COVID-19 vaccine hesitancy among ordinary citizens. In addition to demographic and vaccine-related information, a five-point Likert scale was used to measure vaccine-related misinformation beliefs and how to counter them. Chi-square tests were used to examine the relationship between demographic variables and vaccine acceptance. A binary logistic regression analysis was conducted to identify vaccine hesitancy by different demographic groups. Nonparametric Mann–Whitney and Kruskal–Wallis tests were performed to determine the significance of difference between demographic groups in terms of their vaccine-related misinformation beliefs. Finally, the total misinformation score was computed to examine the correlation between vaccine hesitancy and the total score.

Findings

This study found that nearly half of the respondents were willing to receive COVID-19 vaccine, whereas more than one third of the participants were unsure about taking the vaccine. Demographic variables (e.g., gender, age and education) were found to be significantly related to COVID-19 vaccine acceptance. The results of binary logistic regression analysis showed that respondents who were below 40 years of age, females and those who had lower education attainments had significantly higher odds of vaccine hesitancy. There were significant differences in participants’ vaccine-related misinformation beliefs based on their demographic characteristics, particularly in the case of educational accomplishments. A highly significant negative correlation was found between total misinformation score and vaccine acceptance.

Research limitations/implications

The survey was conducted online, and therefore, it automatically precluded non-internet users from completing the survey. Further, the number of participants from villages was relatively low. Overall, the results may not be representative of the entire population in Bangladesh.

Practical implications

The findings of this paper could guide government agencies and policymakers in devising appropriate strategies to counter COVID-related misinformation to reduce the level of vaccine hesitancy in Bangladesh.

Originality/value

To the authors’ best knowledge, this study is the first to measure the level of COVID-19 vaccine hesitancy and the influence of misinformation in this process among the general public in Bangladesh.

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

Purpose

Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.

Design/methodology/approach

To fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.

Findings

A gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.

Research limitations/implications

The main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.

Practical implications

The study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.

Originality/value

The paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

The impact of human vaccines on bacterial antimicrobial resistance. A review

At present, the dramatic rise in antimicrobial resistance (AMR) among important human bacterial pathogens is reaching a state of global crisis threatening a return to the pre-antibiotic era. AMR, already a significant burden on public health and economies, is anticipated to grow even more severe in the coming decades. Several licensed vaccines, targeting both bacterial (Haemophilus influenzae type b, Streptococcus pneumoniae, Salmonella enterica serovar Typhi) and viral (influenza virus, rotavirus) human pathogens, have already proven their anti-AMR benefits by reducing unwarranted antibiotic consumption and antibiotic-resistant bacterial strains and by promoting herd immunity. A number of new investigational vaccines, with a potential to reduce the spread of multidrug-resistant bacterial pathogens, are also in various stages of clinical development. Nevertheless, vaccines as a tool to combat AMR remain underappreciated and unfortunately underutilized. Global mobilization of public health and industry resources is key to maximizing the use of licensed vaccines, and the development of new prophylactic vaccines could have a profound impact on reducing AMR.

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models

Background

The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy.

Objective

Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media.

Methods

This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine–related information on Twitter.

Results

We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine–related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary.

Conclusions

Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.

Trends in Human Papillomavirus Vaccine Safety Concerns and Adverse Event Reporting in the United States

IMPORTANCE

In the US, safety concern has been identified as a primary barrier to initiating the human papillomavirus (HPV) vaccine. It is unclear if the public's sentiment concerning HPV vaccine safety aligns with postmarketing vaccine safety surveillance data.

OBJECTIVE

To perform a parallel assessment of trends in HPV vaccine safety concerns and HPV vaccine adverse event (AE) reporting.

DESIGN, SETTING, AND PARTICIPANTS

This study was a cross-sectional analysis of the National Immunization Survey (NIS) and Vaccine Adverse Event Reporting System (VAERS). Participants in the NIS were adolescents aged 13 to 17 years. AEs were reported to VAERS by patients, health care clinicians, or other sources. Statistical analysis was performed from October 2020 to May 2021.

MAIN OUTCOMES AND MEASURES

Secular trends in HPV vaccine safety concerns and spontaneous AE reporting for HPV vaccination from 2015 to 2018.

RESULTS

Caregivers of 39 364 unvaccinated adolescents with a mean (SD) age of 15.57 (0.08) years (26 996 White adolescents [62.9%], 22 707 male adolescents [56.1%], 11 392 privately insured [62.6%], and 32 674 above the poverty level [79.3%]) reported their reasons for not initiating the HPV vaccine series in the 2015-2018 NIS. Citing safety concerns as the primary reason for not initiating the HPV vaccine series increased from 13.0% (95% CI, 12.1%-14.0%) in 2015 to 23.4% (95% CI, 21.8%-25.0%) in 2018 (P for trend < .001), equating to a change from 170 046 to 259 157 US adolescents not initiating the vaccine because of safety concerns. The proportion of parents citing safety concerns as the main reason for HPV vaccine hesitancy increased in 30 states. The largest increases (more than 200%) were observed in California, Hawaii, South Dakota, and Mississippi. During 2015 to 2018, 16 621 AE reports following HPV vaccination were reported to VAERS. The AE reporting rate per 100 000 doses distributed decreased from 44.7 in 2015 to 29.4 in 2018 (P for trend < .001). The serious AE reporting rate, including those leading to hospitalizations, disability, life-threatening condition, or death did not change.

CONCLUSIONS AND RELEVANCE

In this descriptive cross-sectional study, a rise in citing safety concerns was observed among parents with HPV vaccine hesitancy, contrary to the nonserious and serious AE reporting trends. These findings suggest an urgent need to combat the rising sentiment of safety concerns among caregivers to increase HPV vaccine confidence.

Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study

BACKGROUND

Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading the public to accept immunization. Many prior studies have associated the diversity of topics discussed by antivaccine advocates with the public's higher engagement with such content. Nonetheless, a comprehensive comparison of discursive topics in pro- and antivaccine content in the engagement-persuasion spectrum remains unexplored.

OBJECTIVE

We aimed to compare discursive topics chosen by pro- and antivaccine advocates in their attempts to influence the public to accept or reject immunization in the engagement-persuasion spectrum. Our overall objective was pursued through three specific aims as follows: (1) we classified vaccine-related tweets into provaccine, antivaccine, and neutral categories; (2) we extracted and visualized discursive topics from these tweets to explain disparities in engagement between pro- and antivaccine content; and (3) we identified how those topics frame vaccines using Entman's four framing dimensions.

METHODS

We adopted a multimethod approach to analyze discursive topics in the vaccine debate on public social media sites. Our approach combined (1) large-scale balanced data collection from a public social media site (ie, 39,962 tweets from Twitter); (2) the development of a supervised classification algorithm for categorizing tweets into provaccine, antivaccine, and neutral groups; (3) the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides; and (4) a multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics. In so doing, we alleviated methodological challenges that have hindered previous analyses of pro- and antivaccine discursive topics.

RESULTS

Our results indicated that antivaccine topics have greater intertopic distinctiveness (ie, the degree to which discursive topics are distinct from one another) than their provaccine counterparts (t₁₂₂=2.30, P=.02). In addition, while antivaccine advocates use all four message frames known to make narratives persuasive and influential, provaccine advocates have neglected having a clear problem statement.

CONCLUSIONS

Based on our results, we attribute higher engagement among antivaccine advocates to the distinctiveness of the topics they discuss, and we ascribe the influence of the vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates.

Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events

Background

Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events.

Objective

The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection.

Methods

This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion.

Results

Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F₁-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories.

Conclusions

This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats.

Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning

In 2019, outbreaks of vaccine-preventable diseases reached the highest number in the US since 1992. Medical misinformation, such as antivaccine content propagating through social media, is associated with increases in vaccine delay and refusal. Our overall goal is to develop an automatic detector for antivaccine messages to counteract the negative impact that antivaccine messages have on the public health. Very few extant detection systems have considered multimodality of social media posts (images, texts, and hashtags), and instead focus on textual components, despite the rapid growth of photo-sharing applications (e.g., Instagram). As a result, existing systems are not sufficient for detecting antivaccine messages with heavy visual components (e.g., images) posted on these newer platforms. To solve this problem, we propose a deep learning network that leverages both visual and textual information. A new semantic- and task-level attention mechanism was created to help our model to focus on the essential contents of a post that signal antivaccine messages. The proposed model, which consists of three branches, can generate comprehensive fused features for predictions. Moreover, an ensemble method is proposed to further improve the final prediction accuracy. To evaluate the proposed model's performance, a real-world social media dataset that consists of more than 30,000 samples was collected from Instagram between January 2016 and October 2019. Our 30 experiment results demonstrate that the final network achieves above 97% testing accuracy and outperforms other relevant models, demonstrating that it can detect a large amount of antivaccine messages posted daily. The implementation code is available at https://github.com/wzhings/antivaccine_detection.

#Bias: The Opportunities and Challenges of Surveys That Recruit and Collect Data of Autistic Adults Online

Internet-based online surveys are a crucial tool for researchers to learn about the understudied and often overlooked population of autistic adults. The recruitment and administration of online surveys can be cheaper, quicker, and have a wider reach compared with more traditional in-person methods. As online surveys become more prevalent, it is important to place strengths in the context of limitations and biases that can arise when recruiting and administering surveys online. In this perspective, we discuss two common issues that often appear in studies that use online tools to recruit and administer surveys to autistic adults and nonautistic volunteers: selection bias and sample identifiability. Selection bias is the distortion in effect estimates (e.g., relative risk, risk ratio, incidence rate) resulting from the factors that influence why a person chose to participate or how the researcher recruits and selects participants in a study. Sampling identifiability is the ability (or inability) to quantify and define the population of interest. We use a case example of an online survey study of suicidal ideation in autistic adults and describe how issues in selection bias and sample identifiability arise and may lead to challenges unique to studying autistic adults. We conclude with recommendations to improve the quality and utility of online survey research in autistic adults. Using online resources to recruit and collect data on autistic adults is an incredible tool with great potential; yet, authors need to consider the limitations, potential biases, and tools to overcome systematic error at each stage of the study.

LAY SUMMARY

What is the purpose of this article?: Our purpose was to describe challenges in conducting and analyzing data from surveys of autistic adults, recruited and completed online.What is already known on the topic?: Health outcomes for autistic adults are understudied by crucial areas of autism research. While researchers are interested in the outcomes of autistic adults, this type of research is difficult because many autistic adults are not formally diagnosed, so not available to recruit for studies through clinic registries. Furthermore, study participation can be a long, inconvenient, and stressful process. It is not surprising then that we are seeing internet surveys of autistic adults become a popular tool to reach this population. We wanted to offer an overview and recommendations of these issues to researchers and people who read research about topics pertaining to autistic adults.What are the perspectives of the authors?: We are epidemiologists at Boston University and the University of Wisconsin-Madison. We both conduct research centered in improving health and well-being for autistic people across the life span. As people who study research methods, we have seen a lot of new research using survey methods. This research is intriguing, but all too often the articles need more information so we can be sure that the research is high quality. We want to share ways to improve this type of research and to help people in understanding the strengths and limitations of online survey research.What do the authors recommend?: We offer a few considerations for researchers working in this area. (1) Make the steps you took to do the research as clear as possible. (2) Be specific about who you intend to study and who you ended up studying. (3) Present the demographics and characteristics of the participants. (4) If possible, consider using data analysis methods to account for selection bias and sample identifiability issues. (5) Do not make statements that are not supported for your study results. (6) Acknowledge that we are at the beginning of studying autistic adults. (7) Advocate for more funding for research in autistic adults.How will these recommendations help autistic adults now or in the future?: Online surveys are an important tool for researchers to generate hypotheses and connect with a wider range of participants. However, online surveys have unique methodological challenges. We hope that this perspective raises the topic of bias and misinterpretation in online surveys and researchers continue to produce valid and meaningful work that is crucial to improving lives of autistic adults.

[Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019]

Objetivo.

Informar sobre la oposición a las vacunas y la información errónea fomentadas en Twitter, destacando las cuentas de Twitter que dirigen estas conversaciones.

Métodos.

Utilizamos el aprendizaje automático supervisado para codificar todos los mensajes publicados en Twitter. En primer lugar, identificamos manualmente los códigos y los temas mediante un enfoque teórico fundamentado y, a continuación, los aplicamos a todo el conjunto de datos de forma algorítmica. Identificamos a los 50 autores más importantes un mes tras otro para determinar las fuentes influyentes de información relacionadas con la oposición a las vacunas.

Resultados.

El período de recopilación de datos fue del 1 de junio al 1 de diciembre del 2019, lo que dio lugar a 356 594 mensajes opuestos a las vacunas. Un total de 129 autores de Twitter reunieron los criterios de autor principal durante al menos un mes. Los autores principales fueron responsables del 59,5% de los mensajes opuestos a las vacunas y detectamos diez temas de conversación. Los temas se distribuyeron de forma similar entre los autores principales y todos los demás autores que declararon su oposición a las vacunas. Los autores principales parecían estar muy coordinados en su promoción de la información errónea sobre cada tema.

Conclusiones.

La salud pública se ha esforzado por responder a la información errónea sobre las vacunas. Los resultados indican que las fuentes de información errónea sobre las vacunas no son tan heterogéneas ni están tan distribuidas como podría parecer a primera vista, dado el volumen de mensajes. Existen fuentes identificables de información errónea, lo que puede ayudar a contrarrestar los mensajes y a fortalecer la vigilancia de la salud pública.

Online Attitudes and Information-Seeking Behavior on Autism, Asperger Syndrome, and Greta Thunberg

The purpose of this study was to examine Internet trends data and sentiment in tweets mentioning autism, Asperger syndrome, and Greta Thunberg during 2019. We used mixed methods in analyzing sentiment and attitudes in viral tweets and collected 1074 viral tweets on autism that were published in 2019 (tweets that got more than 100 likes). The sample from Twitter was compared with search patterns on Google. In 2019, Asperger syndrome was closely connected to Greta Thunberg, as of the tweets specifically mentioning Asperger (from the total sample of viral tweets mentioning autism), 83% also mentioned Thunberg. In the sample of tweets about Thunberg, the positive sentiment expressed that Greta Thunberg was a role model, whereas the tweets that expressed the most negativity used her diagnosis against her and could be considered as cyberbullying. The Google Trends data also showed that Thunberg was closely connected to search patterns on Asperger syndrome in 2019. The study showed that being open about health information while being an active participant in controversial debates might be used against you but also help break stigmas and stereotypes.

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

Vaccine hesitancy and anti-vaccination in the time of COVID-19: A Google Trends analysis

The COVID-19 pandemic has produced many calls for a vaccine. There is growing concern that vaccine hesitancy and anti-vaccination presence will dampen the uptake of a coronavirus vaccine. There are many cited reasons for vaccine hesitancy. Mercury content, autism association, and vaccine danger have been commonly found in anti-vaccination messages. It is also mused that the reduced disease burden from infectious diseases has paradoxically reduced the perceived requirement for vaccine uptake. Our analysis using Google Trends has shown that throughout the pandemic the search interest in a coronavirus vaccine has increased and remained high throughout. Peaks are found when public declarations are made, the case number increases significantly, or when vaccine breakthroughs are announced. Anti-vaccine searches, in the context of COVID-19, have had a continued and growing presence during the pandemic. Contrary to what some may believe, the burden of coronavirus has not been enough to dissuade anti-vaccine searches entirely.

Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review

BACKGROUND

Social media has changed the communication landscape, exposing individuals to an ever-growing amount of information while also allowing them to create and share content. Although vaccine skepticism is not new, social media has amplified public concerns and facilitated their spread globally. Multiple studies have been conducted to monitor vaccination discussions on social media. However, there is currently insufficient evidence on the best methods to perform social media monitoring.

OBJECTIVE

The aim of this study was to identify the methods most commonly used for monitoring vaccination-related topics on different social media platforms, along with their effectiveness and limitations.

METHODS

A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles' titles, abstracts, and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools; ethical considerations; search strategies; periods monitored; geolocalization of content; and sentiments, content, and reach analyses.

RESULTS

This review identified 86 articles on social media monitoring of vaccination, most of which were published after 2015. Although 35 out of the 86 studies used manual browser search tools to collect data from social media, this was time-consuming and only allowed for the analysis of small samples compared to social media application program interfaces or automated monitoring tools. Although simple search strategies were considered less precise, only 10 out of the 86 studies used comprehensive lists of keywords (eg, with hashtags or words related to specific events or concerns). Partly due to privacy settings, geolocalization of data was extremely difficult to obtain, limiting the possibility of performing country-specific analyses. Finally, 20 out of the 86 studies performed trend or content analyses, whereas most of the studies (70%, 60/86) analyzed sentiments toward vaccination. Automated sentiment analyses, performed using leverage, supervised machine learning, or automated software, were fast and provided strong and accurate results. Most studies focused on negative (n=33) and positive (n=31) sentiments toward vaccination, and may have failed to capture the nuances and complexity of emotions around vaccination. Finally, 49 out of the 86 studies determined the reach of social media posts by looking at numbers of followers and engagement (eg, retweets, shares, likes).

CONCLUSIONS

Social media monitoring still constitutes a new means to research and understand public sentiments around vaccination. A wide range of methods are currently used by researchers. Future research should focus on evaluating these methods to offer more evidence and support the development of social media monitoring as a valuable research design.

Vaccines and autism: a preliminary qualitative study on the beliefs of concerned mothers in Italy

Purpose: While a large body of evidence has shown that the administration of the measles-mumps-rubella (MMR) vaccine is not associated with an increased risk of autism spectrum disorder (ASD), a hesitant attitude towards childhood vaccination is still present among the public. In this study, we aim to investigate the mothers’ perceptions of the cause of their child’s ASD in order to increase our understanding of vaccine hesitancy.

Methods: This study draws on the analysis of 18 semi-structured interviews of mothers of children with ASD on the causes of autism.

Results: The interview material was content-analysed. The main themes were 1) childhood vaccines; 2) genetics; 3) specific conditions of the mother or the newborn at the moment of delivery; 4) environmental factors such as the mother’s lifestyle or her diet. The link between vaccines and autism was prevalent. About one third of the mothers reported that their child’s ASD was a consequence of a combination of two or more factors, i.e., childhood vaccines and specific conditions of the newborn or the mother at the moment of delivery.

Conclusion: This study provides preliminary insights into recurring sets of beliefs concerning the causes of ASD among the mothers of affected children.

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

Vaccine hesitancy was one of the ten major threats to global health in 2019, according to the World Health Organisation. Nowadays, social media has an important role in the spread of information, misinformation, and disinformation about vaccines. Monitoring vaccine-related conversations on social media could help us to identify the factors that contribute to vaccine confidence in each historical period and geographical area. We used a hybrid approach to perform an opinion-mining analysis on 1,499,227 vaccine-related tweets published on Twitter from 1st June 2011 to 30th April 2019. Our algorithm classified 69.36% of the tweets as neutral, 21.78% as positive, and 8.86% as negative. The percentage of neutral tweets showed a decreasing tendency, while the proportion of positive and negative tweets increased over time. Peaks in positive tweets were observed every April. The proportion of positive tweets was significantly higher in the middle of the week and decreased during weekends. Negative tweets followed the opposite pattern. Among users with ≥2 tweets, 91.83% had a homogeneous polarised discourse. Positive tweets were more prevalent in Switzerland (71.43%). Negative tweets were most common in the Netherlands (15.53%), Canada (11.32%), Japan (10.74%), and the United States (10.49%). Opinion mining is potentially useful to monitor online vaccine-related concerns and adapt vaccine promotion strategies accordingly.

"Down the Rabbit Hole" of Vaccine Misinformation on YouTube: Network Exposure Study

Background

Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines.

Objective

The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos.

Methods

Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model.

Results

Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos.

Conclusions

YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information.

We need to start thinking about promoting the demand, uptake, and equitable distribution of COVID-19 vaccines NOW!

SARS-CoV-2 (COVID-19) is spreading rapidly within countries around the world, thus necessitating the World Health Organisation (WHO) to project that the peak of the pandemic has not been reached yet. Globally, COVID-19 public health control measures are being implemented; however, promising COVID-19 vaccine candidates are still in the early-stage clinical trials.

Judging from previous vaccine programs around the world and the challenges encountered in the distribution and uptake, there seems to be no guarantee that there will be widespread acceptance and equitable distribution of the new COVID-19 vaccines when they are approved for use.

Therefore, there is an urgent need to start engaging the public to allay their fears and misconceptions with the view to building trust and promoting acceptance and ultimately achieving a potential impact in controlling the pandemic. Borrowing from previously used successful public health strategies, including the application of the health belief model to engage communities, can go a long way in promoting the demand, uptake, and equitable distribution of the COVID-19 vaccine, thereby minimizing the likelihood of vaccine hesitancy.

Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques

Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem.

Undermining Credibility: The Limited Influence of Online Comments to Vaccine-related News Stories

State-sponsored disinformation campaigns increasingly use anti-vaccine comments to not only undermine public health but to also reduce confidence and participation in a democratic society. Despite these dangers, research has not fully explored whether anti-vaccine comments can achieve these effects. To address this gap, an online survey experiment was conducted using a national sample of 1010 U.S. adults. Participants read a mainstream news article discussing the flu vaccine that included random variations of user comments adapted from a documented state-sponsored disinformation campaign. While exposure to anti-vaccine comments did not affect participants' views of vaccines or their willingness to discuss vaccines, participants holding pro-vaccine views reported lower confidence in news organizations and viewed the journalist who authored their article as less credible. These results suggest that anti-vaccine comments may produce effects that align with the goals of state-sponsored disinformation campaigns.

Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019

Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation.Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition.Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes.Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter

Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter.Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set.Results. Manual annotation identified 22% of the data set as antivaccine, of which safety concerns and conspiracies were the most common themes. Seventeen percent of content was identified as provaccine, with roughly equal proportions of vaccine promotion, criticizing antivaccine beliefs, and vaccine safety and effectiveness. Of the 100 LDA topics, 48 contained provaccine sentiment and 28 contained antivaccine sentiment, with 9 containing both.Conclusions. Our updated typology successfully combines manual annotation with machine-learning methods to estimate the distribution of vaccine arguments, with greater detail on the most distinctive topics of discussion. With this information, communication efforts can be developed to better promote vaccines and avoid amplifying antivaccine rhetoric on Twitter.

Knowledge translation and social media: Twitter data analysis of the 2019 Canadian Guideline for Physical Activity throughout Pregnancy

OBJECTIVES

Despite extensive evidence demonstrating the benefits of prenatal physical activity (PA), inefficient knowledge translation may contribute to low rates of PA during pregnancy. This study aimed to examine the impact of the 2019 Canadian Guideline for Physical Activity throughout Pregnancy (hereafter Guideline) on knowledge transmission via Twitter.

METHODS

Tweets containing keywords regarding prenatal PA were mined using the Twitter Application Programming Interface 1 month prior to (PRE), and 2 months following (POST-Month1 and Month2) Guideline release (October 18, 2018). The volume, user and location of Tweets relevant to prenatal PA were analyzed.

RESULTS

In this 3-month period, 19,944 Tweets were collected. After randomization to select 10% of the sample, 1995 Tweets were analyzed. Tweets related to prenatal PA increased following Guideline release (PRE = 318/674 [45%]; POST-Month1 = 377/755 [50%]); however, this was not sustained into POST-Month2 (202/566 [36%]). The main users Tweeting about prenatal PA were categorized as 'General Population' (33%), whereas top users Tweeting about the Guideline were 'Academics' (25%), 'Exercise Specialists' (27%) and 'Medical Professionals' (20%). POST-Guideline, Tweets originated from users in 42 countries (PRE = 28).

CONCLUSIONS

Twitter can be an effective tool for knowledge transmission of PA guidelines to a variety of end-users around the globe.

Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study

INTRODUCTION

From late 2014 through 2015, Scott County, Indiana faced an HIV outbreak triggered by opioid abuse and transition to injection drug use. Investigating the origins, risk factors, and responses related to this outbreak is critical to inform future surveillance, interventions, and policymaking. In response, this retrospective infoveillance study identifies and characterizes user-generated messages related to opioid abuse, heroin injection drug use, and HIV status using natural language processing (NLP) among Twitter users in Indiana during the period of this HIV outbreak.

MATERIALS AND METHODS

Our study consisted of two phases: data collection and processing, and data analysis. We collected Indiana geolocated tweets from the public Twitter API using Amazon Web Services EC2 instances filtered for geocoded messages in the immediate pre and post period of the outbreak. In the data analysis phase we applied an unsupervised machine learning approach using NLP called the Biterm Topic Model (BTM) to identify tweets related to opioid, heroin/injection, and HIV behavior and then examined these messages for HIV risk-related topics that could be associated with the outbreak.

RESULTS

More than 10 million geocoded tweets occurring in Indiana during the immediate pre and post period of the outbreak were collected for analysis. Using BTM, we identified 1350 tweets thought to be relevant to the outbreak and then confirmed 358 tweets using human annotation. The most prevalent themes identified were tweets related to self-reported abuse of illicit and prescription drugs, opioid use disorder, self-reported HIV status, and public sentiment regarding the outbreak. Geospatial analysis found that these messages clustered in population dense areas outside of the outbreak, including Indianapolis and neighboring Clark County.

DISCUSSION

This infoveillance study characterized the social media conversations of communities in Indiana in the pre and post period of the 2015 HIV outbreak. Behavioral themes detected reflect discussion about risk factors related to HIV transmission stemming from opioid and heroin abuse for priority populations, and also help identify community attitudes that could have motivated or detracted the use of HIV prevention methods, along with helping identify factors that can impede access to prevention services.

CONCLUSIONS

Infoveillance approaches, such as the analysis conducted in this study, represent a possibly strategy to detect "signal" of the emergence of risk factors associated with an outbreak though may be limited in their scope and generalizability. Our results, in conjunction with other forms of public health surveillance, can leverage the growing ubiquity of social media platforms to better detect opioid-related HIV risk knowledge, attitudes and behavior, as well as inform future prevention efforts.

Acceptability of an inactivated influenza vaccine delivered by microneedle patch: Results from a phase I clinical trial of safety, reactogenicity, and immunogenicity

OBJECTIVE

This study sought to evaluate the acceptability of inactivated influenza vaccine delivered by microneedle patch (MNP) in comparison to inactivated influenza vaccine (IIV) delivered by hypodermic needle.

DESIGN, SETTING, AND PARTICIPANTS

From the general population of Atlanta, Georgia, we screened 112 and enrolled 100 healthy adult subjects ages 18 to 49 years. Main Outcome(s) and Measure(s). Our participants were randomized to 4 groups of 25 per arm: (1) IIV by MNP administered by healthcare worker (HCW), (2) IIV by MNP self-administered by study participants, (3) IIV by intramuscular (IM) injection administered by HCW or (4) placebo by MNP administered by HCW. We administered four questionnaires: at Day 0 before and after study product delivery, and at Days 8 and 28.

RESULTS

At baseline, 98.6% of participants receiving MNP vaccination reported an overall positive experience with MNPs, compared to 86.4% for participants receiving IM vaccination. For future influenza vaccination, study participants (N = 99) preferred MNP (n = 65, 69.9%) to injections or nasal spray (n = 20, 21.5%), and the preference for MNP increased from Day 0 to Day 28. Factor analyses resulted in two scaled measures including MNP Use Perceptions (a = 0.799, n = 5 items) and MNP Perceived Convenience (a = 0.844, n = 4 items) that were included in longitudinal assessments; while findings reflect significant differences across treatment groups on mean scores for ease of use, MNP perceived protection, MNP reliability, and MNP selection knowledge, all groups reported their belief that influenza vaccination by MNP would be reliable and protective, as well as easy-to-use and convenient.

CONCLUSIONS AND RELEVANCE

Most participants were accepting of IIV vaccination by MNP and preferred it to injection. Delivery of IIV by MNP may help increase vaccination coverage.

Unvaccinated children as community parasites in National Qualitative Study from Turkey

Background

This national qualitative study explores (1) the experiences, observations, and opinions of health care workers (HCWs) about beliefs, socioeconomic, cultural, and environmental characteristics of parents refusing vaccination and (2) regional differences in the identified risk factors; (3) recommended solutions to improve vaccine acceptance in each of 12 regions in Turkey.

Methods

In total, we carried out 14 individual semi-structured in-depth interviews and 10 focus group discussions with 163 HCWs from 36 provinces. A thematic analysis was performed to explore HCWs’ observations about the parents’ decisions to reject vaccination and possible solutions for vaccine advocacy.

Results

Within the analyzed data framework, vaccine refusal statements could be defined as vaccine safety, the necessity of vaccines, assumptions of freedom of choice, health workers’ vaccine hesitancy, lack of information about national vaccination schedule and components, not trusting the health system, anti-vaccine publications in social media and newspapers, and refugees. Suggestions based on the HCWs suggestions can be summarized as interventions including (1) creating visual cards with scientific data on vaccine content and disease prevention and using them in counseling patients, (2) writing the vaccine components in a way understandable to ordinary people, (3) highlighting the national quality control and production in the vaccine box and labels, (4) conducting interviews with community opinion leaders, (5) training anti-vaccine HCWs with insufficient scientific knowledge and (6) reducing the tax of parents whose children are fully and punctually vaccinated.

Conclusions

The solution to vaccine rejection begins with the right approaches to vaccination during pregnancy. Prepared written and visual information notes should present the information as “vaccination acceptance” rather than “vaccination refusal”. Further studies on vaccine refusal rates should be carried out in various regions of the world so that region-specific actions are implemented to decrease the anti-vaxxer movement and to prevent an outbreak of infectious diseases.

Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks

The human papillomavirus (HPV) vaccine protects adolescents and young adults from 9 high-risk HPV virus types that cause 90% of cervical and anal cancers and 70% of oropharyngeal cancers. This study extends our previous research analyzing online content concerning the HPV vaccination in social media platforms used by young adults, in which we used Pathfinder network scaling and methods of distributional semantics to characterize differences in knowledge organization reflected in consumer- and expert-generated online content. The current study extends this approach to evaluate HPV vaccine perceptions among young adults who populate Reddit, a major social media platform. We derived Pathfinder networks from estimates of semantic relatedness obtained by learning word embeddings from Reddit posts and compared these to networks derived from human expert estimation of the relationship between key concepts. Results revealed that users of Reddit, predominantly comprising young adults in the vaccine catch up age-group 18 through 26 years of age, perceived the HPV vaccine domain from a virus-framed perspective that could impact their lifestyle choices and that their awareness of the HPV vaccine for cancer prevention is also lacking. Further differences in knowledge structures were elucidated, with implications for future health communication initiatives.