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.

Youth account for almost half of all new HIV infections in the United States. Adherence to antiretroviral treatment (ART) is critical for successful management, yet reported adherence rates for youth are often low. This study pilot-tested “+CLICK,” an innovative, web-based, adherence intervention for HIV-positive youth as an adjunct to traditional clinic-based, self-management education. The theory-based application, developed for HIV-infected youth, 13–24 years of age, provides tailored activities addressing attitudes, knowledge, skills, and self-efficacy related to ART adherence. HIV-positive youth (N=10) pilot-tested “+CLICK” to assess usability (ease of use, credibility, understandability, acceptability, motivation) and short-term psychosocial outcomes (importance and self-efficacy related to ART adherence) using a single-group, pre-/post-test study design in a hospital-based pediatric clinic (n=8) and home (n=2) location. Youth were mostly female (80%) and Black (80%). Mean age was 17.8 years (SD=2.65, range 14–22). All were infected perinatally and had been living with HIV all their lives. Most learned their HIV status by age 10 years. Sixty percent reported an undetectable viral load, whilst 10% reported a viral load of over 50,000. Half (50%) reported a normal CD4 count, whilst 20% reported having low CD4 (90%). Most (70%) indicated they would use “+CLICK” again. Short-term psychosocial outcomes indicate significant increase in medication adherence self-efficacy (p<0.05), perceived importance of taking antiretroviral medicine close to the right time every day (p<0.05), and knowledge about HIV and adherence (p<0.01). Other psychosocial variables and behavioral intentions were not significantly impacted. Results suggest that “+CLICK” has the potential to affect psychological antecedents to ART adherence. Further research on long-term and behavioral effects is indicated prior to broader dissemination into clinical practice.