Use of Deep Learning to Analyze Social Media Discussions about the Human Papillomavirus Vaccine

Jingcheng Du, Chongliang Luo, Ross Shegog, Jiang Bian, Rachel M. Cunningham, Julie A. Boom, Gregory A. Poland, Yong Chen, Cui Tao

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Importance: Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies. Objective: To develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media. Design, Setting, and Participants: This retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018. Main Outcomes and Measures: The prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracted from their profiles, and geographic analyses were performed to identify the clustering of public perceptions of the HPV vaccine. Results: A total of 1431463 English-language posts from 486116 unique usernames were collected. Deep learning algorithms achieved F-1 scores ranging from 0.6805 (95% CI, 0.6516-0.7094) to 0.9421 (95% CI, 0.9380-0.9462) in mapping discussions to the constructs of behavior change theories. LOESS revealed trends in constructs; for example, prevalence of perceived barriers, a construct of HBM, deceased from its apex in July 2015 (56.2%) to its lowest prevalence in October 2018 (28.4%; difference, 27.8%; P <.001); Positive attitudes toward the HPV vaccine, a construct of TPB, increased from early 2017 (30.7%) to 41.9% at the end of the study (difference, 11.2%; P <.001), while negative attitudes decreased from 42.3% to 31.3% (difference, 11.0%; P <.001) during the same period. Interstate variations in public perceptions of the HPV vaccine were also identified; for example, the states of Ohio and Maine showed a relatively high prevalence of perceived barriers (11531 of 17106 [67.4%] and 1157 of 1684 [68.7%]) and negative attitudes (9655 of 17197 [56.1%] and 1080 of 1793 [60.2%]). Conclusions and Relevance: This cohort study provided a good understanding of public perceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level..

Original languageEnglish
Article number22025
JournalJAMA Network Open
Volume3
Issue number11
DOIs
StatePublished - Nov 13 2020

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