Abstract
Recently, human papilloma virus (HPV) has been implicated to cause several throat and oral cancers and HPV is established to cause most cervical cancers. A human papilloma virus vaccine has been proven successful to reduce infection incidence in FDA clinical trials, and it is currently available in the USA. Current intervention policy targets adolescent females for vaccination; however, the expansion of suggested guidelines may extend to other age groups and males as well. This research takes a first step toward automatically predicting personal beliefs, regarding health intervention, on the spread of disease. Using linguistic or statistical approaches, sentiment analysis determines a text’s affective content. Self-reported HPV vaccination beliefs published in web and social media are analyzed for affect polarity and leveraged as knowledge inputs to epidemic models. With this in mind, we have developed a discrete-time model to facilitate predicting impact on the reduction of HPV prevalence due to arbitrary age- and gender-targeted vaccination schemes.
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Notes
- 1.
Naive Bayes (5-gram character smoothing) and Language-Model-based (8-gram) classifiers where also evaluated; however, they achieved a maximum 69% accuracy with domain-specific training.
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Acknowledgements
This work was partially supported by the Technosocial Predictive Analytics Initiative, part of the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle for DOE under contract DE-ACO5-76RLO 1830.
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Corley, C.D., Mihalcea, R., Mikler, A.R., Sanfilippo, A.P. (2011). Predicting Individual Affect of Health Interventions to Reduce HPV Prevalence. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_18
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