Key Points
Question
Can public perceptions of the human papillomavirus (HPV) vaccine be accessed from the perspective of behavior change theories by mining social media data with machine learning algorithms?
Findings
This cohort study included 1 431 463 English-language posts about the HPV vaccine from 486 116 unique usernames from a social media platform. An increase in HPV vaccine–related discussions was found, and the results suggest temporal and geographic variations in public perceptions of the HPV vaccine.
Meaning
The findings of this study suggest that social media and machine learning algorithms can serve as a complementary approach to inform public health surveillance and understanding and help to design targeted educational and communication programs that increase HPV vaccine acceptance.
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.
Our aim was to characterize health beliefs about the human papillomavirus (HPV) vaccine in a large set of Twitter posts (tweets). We collected a Twitter data set related to the HPV vaccine from 1 January 2014, to 31 December 2017. We proposed a deep-learning-based framework to mine health beliefs on the HPV vaccine from Twitter. Deep learning achieved high performance in terms of sensitivity, specificity, and accuracy. A retrospective analysis of health beliefs found that HPV vaccine beliefs may be evolving on Twitter.
The Parent Attitudes about Childhood Vaccines (PACV) survey is a validated instrument for identifying vaccine-hesitant parents; however, a Spanish version is not available. Utilizing the WHO framework for translating survey instruments, we used an iterative process for developing the Spanish PACV that included forward translation, expert panel review, back translation and pre-testing that utilized cognitive interviewing. We made revisions to the Spanish PACV at each step, focusing on addressing inclusivity, readability, clarity and conceptual equivalence. The expert panel was comprised of 6 Spanish-speaking medical and research professionals who worked alongside 3 study team members. Pre-testing was conducted using convenience sampling of Spanish-speaking parents (N = 35) who had a child receiving care at the residents' continuity clinic at Texas Children's Hospital. Most pre-testing participants were married (80.6%), mothers (97.1%), ≥30 years of age (88.2%) and had a high school education or less (70.6%). While the majority of participants stated the survey was easy to complete, the translation of 5 PACV items was further revised to improve interpretability. We conclude that the final Spanish PACV is conceptually equivalent and culturally appropriate for most Hispanic populations.
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