What is the most useful tool in HPV vaccine promotion? Results from an experimental study

Abstract This study aimed to quantify human papillomavirus (HPV) vaccine Twitter messaging addressing gay, bisexual and other men who have sex with men (GB+MSM) and describes messaging by vaccine sentiment (attitudes towards vaccine) and characteristics (topic of messaging). Between August 2014 and July 2015, we collected 193 379 HPV-related tweets and classified them by vaccine sentiment and characteristics. We analysed a subsample of tweets containing the terms ‘gay’, ‘bisexual’ and ‘MSM’ (N = 2306), and analysed distributions of sentiment and characteristics using chi-square. HPV-related tweets containing GB+MSM terms occupied 1% of our sample. The subsample had a largely positive vaccine sentiment. However, a proportion of ‘gay’ and ‘bisexual’ tweets did not mention the vaccine, and a proportion of ‘gay’ and ‘MSM’ tweets had a negative sentiment. Topics varied by GB+MSM term—HPV risk messaging was prevalent in ‘bisexual’ (25%) tweets, and HPV transmission through sex/promiscuity messaging was prevalent in ‘gay’ (18%) tweets. Prevention/protection messaging was prevalent only in ‘MSM’ tweets (49%). Although HPV vaccine sentiment was positive in GB+MSM messaging, we identified deficits in the volume of GB+MSM messaging, a lack of focus on vaccination, and a proportion of negative tweets. While HPV vaccine promotion has historically focused on heterosexual HPV transmission, there are opportunities to shape vaccine uptake in GB+MSM through public health agenda setting using social media messaging that increases knowledge and minimizes HPV vaccine stigma. Social media-based HPV vaccine promotion should also address the identities of those at risk to bolster vaccine uptake and reduce the risk of HPV-attributable cancers.

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Who fears the HPV vaccine, who doesn’t, and why? An experimental study of the mechanisms of cultural cognition.

Law and Human Behavior ◽

10.1007/s10979-009-9201-0 ◽

2010 ◽

Vol 34 (6) ◽

pp. 501-516 ◽

Cited By ~ 185

Author(s):

Dan M. Kahan ◽

Donald Braman ◽

Geoffrey L. Cohen ◽

John Gastil ◽

Paul Slovic

Keyword(s):

Experimental Study ◽

Hpv Vaccine ◽

Cultural Cognition

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

10.2196/preprints.26478 ◽

2020 ◽

Author(s):

Jingcheng Du ◽

Sharice Preston ◽

Hanxiao Sun ◽

Ross Shegog ◽

Rachel Cunningham ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Social Media ◽

Human Papillomavirus ◽

Convolutional Neural Network ◽

Hpv Vaccine ◽

Support Vector ◽

Safety Issues ◽

Vaccine Promotion

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

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Translating self-persuasion into an adolescent HPV vaccine promotion intervention for parents attending safety-net clinics

Patient Education and Counseling ◽

10.1016/j.pec.2016.11.014 ◽

2017 ◽

Vol 100 (4) ◽

pp. 736-741 ◽

Cited By ~ 5

Author(s):

Austin S. Baldwin ◽

Deanna C. Denman ◽

Margarita Sala ◽

Emily G. Marks ◽

L. Aubree Shay ◽

...

Keyword(s):

Hpv Vaccine ◽

Safety Net ◽

Safety Net Clinics ◽

Vaccine Promotion

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

Journal of Medical Internet Research ◽

10.2196/26478 ◽

2021 ◽

Vol 23 (8) ◽

pp. e26478

Author(s):

Jingcheng Du ◽

Sharice Preston ◽

Hanxiao Sun ◽

Ross Shegog ◽

Rachel Cunningham ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Social Media ◽

Human Papillomavirus ◽

Convolutional Neural Network ◽

Hpv Vaccine ◽

Support Vector ◽

Social Media Platforms ◽

Vaccine Promotion

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.

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HPV Vaccine Promotion: Does Referring to Both Cervical Cancer and Genital Warts Affect Intended and Actual Vaccination Behavior?

Women s Health Issues ◽

10.1016/j.whi.2010.08.004 ◽

2011 ◽

Vol 21 (1) ◽

pp. 71-79 ◽

Cited By ~ 43

Author(s):

Ilona Juraskova ◽

Royena Abdul Bari ◽

Michaeley Therese O’Brien ◽

Kirsten Jo McCaffery

Keyword(s):

Cervical Cancer ◽

Hpv Vaccine ◽

Genital Warts ◽

Vaccination Behavior ◽

Vaccine Promotion

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Practice-, provider- and patient-level facilitators of and barriers to HPV vaccine promotion and uptake in Georgia: a qualitative study of healthcare providers’ perspectives

Health Education Research ◽

10.1093/her/cyaa026 ◽

2020 ◽

Vol 35 (6) ◽

pp. 512-523

Author(s):

Milkie Vu ◽

Adrian R King ◽

Hyun Min Jang ◽

Robert A Bednarczyk

Keyword(s):

Side Effects ◽

Sexual Activity ◽

Hpv Vaccine ◽

Information Exchange ◽

Healthcare Providers ◽

Appointment Scheduling ◽

Vaccine Uptake ◽

Vaccine Preventable Diseases ◽

Patient Level ◽

Vaccine Promotion

Abstract Georgia experiences higher human papillomavirus (HPV)-associated cancer burden and lower HPV vaccine uptake compared with national estimates. Using the P3 model that concomitantly assesses practice-, provider- and patient-level factors influencing health behaviors, we examined facilitators of and barriers to HPV vaccine promotion and uptake in Georgia. In 2018, we conducted six focus groups with 55 providers. Questions focused on multilevel facilitators of and barriers to HPV vaccine promotion and uptake. Our analysis was guided by the P3 model and a deductive coding approach. We found that practice-level influences included organizational priorities of vaccinations, appointment scheduling, immunization registries/records, vaccine availability and coordination with community resources. Provider-level influences included time constraints, role, vaccine knowledge, self-efficacy to discuss HPV vaccine and vaccine confidence. Patient-level influences included trust, experiences with vaccine-preventable diseases, perceived high costs, perceived side effects and concerns with sexual activity. Findings suggest that interventions include incentives to boost vaccine rates and incorporate appointment scheduling technology. An emphasis should be placed on the use of immunization registries, improving across-practice information exchange, and providing education for providers on HPV vaccine. Patient–provider communication and trust emerge as intervention targets. Providers should be trained in addressing patient concerns related to costs, side effects and sexual activity.

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