Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States

Abstract

ObjectivesTo test the feasibility of using Twitter data to assess determinants of consumers' health behavior towards Human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). MethodsWe used three Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers' discussions. We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS). ResultsWe collected 2,846,495 tweets and analyzed 335,681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is "cervical cancer screening"; while in promotional tweets, the most popular topic is to increase awareness of "HPV causes cancer". 87 out of the 122 topics are correlated between promotional information and consumers' discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. 35 topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions. ConclusionNot only mining Twitter to assess consumers' health behaviors can obtain results comparable to surveys but can yield additional insights via a theory-driven approach. Limitations exist; nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape. BACKGROUND and SIGNIFICANCEHuman papillomavirus (HPV) is the most common sexually transmitted disease (STD) in the United States (US) [1]. Although HPV infections are transient, persistent infection can lead to cancer. An estimated 33,700 new patients are diagnosed with HPV-associated cancers (e.g., anal, penile, cervical, and oral cancers) each year [2] in US. HPV vaccine is effective in preventing most of these HPV-related cancers for individuals in early age [3]. Nevertheless, in 2017, only 48.6% of US adolescents received recommended HPV vaccination series, and 65.5% received ≥1dose of the series [4]. HPV vaccination coverage also varies greatly by state. Only three states (i.e., District of Columbia: 91.9%, Rhode Island: 88.6%, and Massachusetts: 81.9%) have more than 80% coverage for the first dose, while the bottom three states (i.e., Kentucky: 49.6%, and Mississippi: 49.6%, Wyoming: 46.9%) have coverage rates less than 50% [4]. There is a huge public health needs to increase the awareness of HPV-related issues to promote HPV vaccination.To increase HPV vaccination initiation and coverage, we first need to understand factors that affect people's health behavior towards vaccination uptake. Recognized by the Integrated

Persistent digital divide in health-related internet use among cancer survivors: findings from the Health Information National Trends Survey, 2003–2018

“…Third, our analysis does not account for type of cancer, which has been shown to be associated with cancer information seeking behavior [60]. Fourth, the data are cross-sectional precluding any inference on causation highlighting the need for other types of studies to complement results from surveys, for example, patterns of information use can be gleaned using internet search data [4] and mined from social media platforms [61] to explore questions about the specific topics discussed, relation between promotional information and discussions, and the relationship of these patterns to survey results from HINTS. Lastly, the response rate in HINTS was low, but it was similar in other national surveys [62,63].…”

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

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Detecting adherence to the recommended childhood vaccination schedule from user-generated content in a US parenting forum

“…Other studies focused on identifying users with strong opinions on vaccination (e.g., anti-vaccination and pro-vaccination) [ 19 ], but this kind of classification overlooks users with more nuanced positions who may be more suitable for intervention. Recent progresses in this direction have employed machine learning methods to map the content of tweets to constructs of validated health behavior models [ 20 , 21 ] or classify users depending on their intention to receive a specific vaccine [ 22 ]. To date, we are not aware of studies attempting to automatically determine the adherence of parents to alternative childhood vaccination schedules from user-generated content.…”