Hansi Zhang1, Christopher Wheldon2, Adam G Dunn3, Cui Tao4, Jinhai Huo5, Rui Zhang6, Mattia Prosperi7, Yi Guo1, Jiang Bian1. 1. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA. 2. Department of Social and Behavioral Sciences, College of Public Health, Temple University, Philadelphia, Pennsylvania, USA. 3. Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia. 4. School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA. 5. Department of Health Services Research, Management and Policy, University of Florida, Gainesville, Florida, USA. 6. Institute for Health Informatics and Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, Minnesota, USA. 7. Department of Epidemiology, University of Florida, Gainesville, Florida, USA.
Abstract
OBJECTIVES: The study sought to test the feasibility of using Twitter data to assess determinants of consumers' health behavior toward human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). MATERIALS AND METHODS: We used 3 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). RESULTS: We 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." A total of 87 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. Thirty-five 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. CONCLUSIONS: Mining Twitter to assess consumers' health behaviors can not only obtain results comparable to surveys, but also 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.
OBJECTIVES: The study sought to test the feasibility of using Twitter data to assess determinants of consumers' health behavior toward human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). MATERIALS AND METHODS: We used 3 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). RESULTS: We 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." A total of 87 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. Thirty-five 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. CONCLUSIONS: Mining Twitter to assess consumers' health behaviors can not only obtain results comparable to surveys, but also 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.
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