Does social influence affect COVID-19 vaccination intention among the unvaccinated?

Conformist social influence is a double-edged sword when it comes to vaccine promotion. On the one hand, social influence may increase vaccine uptake by reassuring the hesitant about the safety and effectiveness of the vaccine; on the other, people may forgo the cost of vaccination when the majority is already vaccinated – giving rise to a public goods dilemma. Here, we examine whether available information on the percentage of double-vaccinated people affects COVID-19 vaccination intention among unvaccinated people in Turkey. In an online experiment, we divided participants (n = 1013) into low, intermediate, and high social influence conditions, reflecting the government’s vaccine promotion messages. We found that social influence did not predict COVID-19 vaccination intention, but psychological reactance and collectivism did. People with higher reactance (intolerance of others telling one what to do and being sceptical of consensus views) had lower vaccination intention, whilst people with higher collectivism (how much a person considers group benefits over individual success) had higher vaccination intention. Our findings suggest that advertising the percentage of double-vaccinated people is not sufficient to trigger a cascade of others getting themselves vaccinated. Diverse promotion strategies reflecting the heterogeneity of individual attitudes could be more effective.

A Shot of Faith—Analyzing Vaccine Hesitancy in Certain Religious Communities in the United States

Vaccine hesitancy in the United States continues to hamper ongoing coronavirus vaccination efforts. One set of populations with higher-than-average initial rates of vaccine hesitancy are certain religious groups, such as white evangelicals, African-American Protestants, and Hispanic Catholics. This article discusses the reasons underlying vaccine hesitancy in these populations, focusing on new trends in religious, political, and ideological beliefs that may influence vaccine acceptance. By using recent data and empirical case studies, this article describes how these trends could hinder the effectiveness of certain vaccine promotion strategies while also improving the potential efficacy of other forms of vaccine promotion, such as faith-based outreach. (100)

Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

Vaccine-Hesitant Parents' Considerations Regarding Covid-19 Vaccination of Adolescents

Introduction: Israel led a rapid vaccine rollout against COVID-19, leading to a local remission of the epidemic and rolling back of most public health measures. Further vaccination of 12-15-year-olds may be hindered by public perceptions of the necessity and safety of vaccination. Methods: we examined the considerations of vaccine hesitant parents (VHPs) regarding vaccination of children against COVID-19. The responses of 456 parents were surveyed and analyzed before FDA authorization of vaccination of children. Results: parents who were vaccinated against COVID-19 were more likely to intend to vaccinate their children (r=-0.466, p<0.01). Low accessibility of vaccination may be a dissuading factor for VHPs more inclined to vaccinate. Vaccine efficacy and gaining a "Green Pass" were positively associated with an intention to vaccinate and statistically significant. VHPs inclined not to vaccinate indicated short development time and possible long term effects as dissuading factors. Discussion: vaccine promotion should be tailored for VHPs' positive and negative considerations for higher uptake.

The social experience of participation in a COVID-19 vaccine trial: Subjects’ motivations, others’ concerns, and insights for vaccine promotion

BackgroundVaccine hesitancy could undermine the effectiveness of COVID-19 vaccination programs. Knowledge about people’s lived experiences regarding COVID-19 vaccination can enhance vaccine promotion and increase uptake.AimTo use COVID-19 vaccine trial participants’ experiences to identify key themes in the lived experience of vaccination early in the vaccine approval and distribution process.MethodsWe interviewed 31 participants in the Iowa City, Iowa US site of the Pfizer/BioNTech COVID-19 vaccine phase 3 clinical trial. While trial participation differs from clinical receipt of an approved vaccine in key ways, it offers the first view of people’s lived experiences of potentially receiving a COVID-19 vaccine. The trial context is also useful since decision-making about vaccination and medical research participation often involve similar hopes and concerns, and because the public appears to view even approved COVID-19 vaccines as experimental given their novelty. Semi-structured interviews addressed subjects’ experiences, including decision-making and telling others about their trial participation. We analyzed verbatim transcripts of these interviews thematically and identified common themes relevant for vaccination decision-making.ResultsParticipants across demographic groups, including age, sex/gender, race/ethnicity, and political affiliation, described largely similar experiences. Key motivations for participation included ending the pandemic/restoring normalcy, protecting oneself and others, doing one’s duty, promoting/modeling vaccination, and expressing aspects of identity like being a helper, career-related motivations, and support of science/vaccines. Participants often felt uniquely qualified to help via trial participation due to personal attributes like health, sex/gender or race/ethnicity. They reported hearing concerns about side effects and the speed and politicization of vaccine development. Participants responded by normalizing and contextualizing side effects, de-politicizing vaccine development, and explaining how the rapid development process was nevertheless safe.ConclusionThese findings regarding participants’ reported motivations for trial participation and interactions with concerned others can be incorporated into COVID-19 vaccine promotion messaging aimed at similar populations.

Utilizing machine learning-based approaches for the detection and classification of human papillomavirus (HPV) vaccine misinformation: Infodemiology Study of Reddit Discussions (Preprint)

BACKGROUND The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information and thus create obstacles for vaccine promotion. OBJECTIVE To develop and evaluate an intelligent automated protocol to identify and classify HPV vaccine misinformation on social media, using machine learning (ML)-based methods. METHODS Reddit posts (2007-2017, n=28,121) were compiled that contained human papillomavirus (HPV) vaccine related keywords. A random subset (n=2200) was manually labeled for misinformation, serving as a gold standard corpus for evaluation. Five ML-based algorithms, including support vector machines (SVM), logistics regression (LR), extremely randomized trees (ET), convolutional neural network (CNN) and recurrent neural network (RNN), designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. RESULTS A convolutional neural network model achieved the highest AUC at 0.7943. Of 28,121 Reddit posts, 7,207 (25.63%) were classified as vaccine misinformation with discussions about general safety issues identified as the leading type misinformed posts (37%). CONCLUSIONS ML-based approaches are effective in the identification and classification of HPV vaccine misinformation from Reddit and may be generalizable to other social media platforms. ML -based methods may provide the capacity and utility to meet the challenge for intelligent automated monitoring and classification of public health misinformation in social media networks. The timely identification of vaccine misinformation online is a first step for misinformation correction and vaccine promotion. CLINICALTRIAL