Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content

Jessie Chin; Chieh-Li Chin; Sakshi Panday; Anoosheh Ghazanfari; Ganesh Jagadeesan; Ziyi Wang; Ana Ontengco; Amy Chang; Bing Liu; Alan Schwartz; Rachel Caskey

doi:10.1177/2327857920091069

Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content

Jessie Chin, Chieh-Li Chin, Sakshi Panday, Anoosheh Ghazanfari, Ganesh Jagadeesan, Ziyi Wang, Ana Ontengco, Amy Chang, Bing Liu, Alan Schwartz, Rachel Caskey

Research output: Contribution to journal › Conference article › peer-review

Abstract

The goal of the study is to identify and track the dissemination patterns of true and false information about Human Papillomavirus (HPV) vaccines on twitter from 2013 to 2017. We applied a patient-driven HPV-vaccine risk lexicon combining with natural language processing (NLP) models and network analyses to explore the spread of verified true and false HPV-vaccine information. The explorative analyses showed different dissemination patterns of the most popular verified true and false messages about HPV vaccines, which false messages went viral than the true messages. Implications on detecting false HPV-vaccine related information were also discussed.

Original language	English (US)
Pages (from-to)	312-316
Journal	Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care
Volume	9
Issue number	1
DOIs	https://doi.org/10.1177/2327857920091069
State	Published - Sep 2020

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Chin, J., Chin, C.-L., Panday, S., Ghazanfari, A., Jagadeesan, G., Wang, Z., Ontengco, A., Chang, A., Liu, B., Schwartz, A., & Caskey, R. (2020). Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 9(1), 312-316. https://doi.org/10.1177/2327857920091069

Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content. / Chin, Jessie; Chin, Chieh-Li; Panday, Sakshi et al.
In: Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, Vol. 9, No. 1, 09.2020, p. 312-316.

Research output: Contribution to journal › Conference article › peer-review

Chin, J, Chin, C-L, Panday, S, Ghazanfari, A, Jagadeesan, G, Wang, Z, Ontengco, A, Chang, A, Liu, B, Schwartz, A & Caskey, R 2020, 'Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content', Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, vol. 9, no. 1, pp. 312-316. https://doi.org/10.1177/2327857920091069

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abstract = "The goal of the study is to identify and track the dissemination patterns of true and false information about Human Papillomavirus (HPV) vaccines on twitter from 2013 to 2017. We applied a patient-driven HPV-vaccine risk lexicon combining with natural language processing (NLP) models and network analyses to explore the spread of verified true and false HPV-vaccine information. The explorative analyses showed different dissemination patterns of the most popular verified true and false messages about HPV vaccines, which false messages went viral than the true messages. Implications on detecting false HPV-vaccine related information were also discussed.",

author = "Jessie Chin and Chieh-Li Chin and Sakshi Panday and Anoosheh Ghazanfari and Ganesh Jagadeesan and Ziyi Wang and Ana Ontengco and Amy Chang and Bing Liu and Alan Schwartz and Rachel Caskey",

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AU - Ghazanfari, Anoosheh

AU - Jagadeesan, Ganesh

AU - Wang, Ziyi

AU - Ontengco, Ana

AU - Chang, Amy

AU - Liu, Bing

AU - Schwartz, Alan

AU - Caskey, Rachel

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Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content

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