Machine Learning Clinical Trial
Official title:
A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time
NCT number | NCT05442762 |
Other study ID # | ECT2112016948 |
Secondary ID | |
Status | Withdrawn |
Phase | |
First received | |
Last updated | |
Start date | March 1, 2022 |
Est. completion date | June 24, 2022 |
Verified date | June 2022 |
Source | Fudan University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
History and scientific evidence show that it is critical to maintain public trust and confidence in vaccination. Any crisis in confidence has the potential to cause significant disruption and a detrimental impact on vaccination. Vaccine hesitancy is a complex and context-specific issue that varies across time, place, and vaccines. It has been cited by World Health Organization(WHO) as one of the top ten threats to global health in 2019. Coronavirus disease(COVID-19) pandemic may change public confidence in vaccines. Therefore, it is necessary to establish a surveillance system to monitor vaccine confidence and hesitancy in real time. To date, a growing body of literature has used social media platforms such as Twitter and weico for public health research. Large amounts of real time data posted on social media platforms can be used to quickly identify the public's attitudes on vaccines, as a way to support health communication and health promotion, messaging. However, textual data on social media is difficult to be analyzed. Recent progress in machine learning makes it possible to automatically analyze textual data on social media in real time. In this study, the investigators will establish a social media surveillance and analysis platform on vaccines, develop a series of machine learning models to monitor vaccine confidence and early detect emerging vaccine-related risks, and assess public communication around vaccines. The investigators will assess the temporal and spatial distribution of vaccine confidence and hesitancy globally using Twitter data and in China using weico data, for all vaccines and Human Papilloma Virus(HPV) vaccine, respectively. Our study will guide the design of effective health communication strategies to improve vaccine confidence.
Status | Withdrawn |
Enrollment | 0 |
Est. completion date | June 24, 2022 |
Est. primary completion date | June 1, 2022 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | N/A and older |
Eligibility | Inclusion Criteria: - Tweets and weico posts related to vaccines - Published in 2015-2022 - English tweets - Tweets/posts from personal accounts. Exclusion Criteria: - Tweets/posts from news, organization accounts, or authenticated users - Non English tweets. |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
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Fudan University | Merck Sharp & Dohme LLC |
Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi M, Shah Z. Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study. J Med Internet Res. 2020 Apr 21;22(4):e19016. doi: 10.2196/19016. — View Citation
Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res, 2003, 3:993-1022.
de Figueiredo A, Simas C, Karafillakis E, Paterson P, Larson HJ. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. Lancet. 2020 Sep 26;396(10255):898-908. doi: 1 — View Citation
Devlin J, Chang M-W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, 2018, arXiv:181004805.
Larson HJ, de Figueiredo A, Xiahong Z, Schulz WS, Verger P, Johnston IG, Cook AR, Jones NS. The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey. EBioMedicine. 2016 Oct;12:295-301. doi: 10.1016/j.ebiom.2016.08.042. Epub 2016 S — View Citation
Larson HJ, Jarrett C, Eckersberger E, Smith DM, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine. 2014 Apr 17;32(19):2150-9. doi: 10.1016/j.vaccine.2014.01.081. Epub 2014 Mar 2. Review. — View Citation
Larson HJ, Jarrett C, Schulz WS, Chaudhuri M, Zhou Y, Dube E, Schuster M, MacDonald NE, Wilson R; SAGE Working Group on Vaccine Hesitancy. Measuring vaccine hesitancy: The development of a survey tool. Vaccine. 2015 Aug 14;33(34):4165-75. doi: 10.1016/j.vaccine.2015.04.037. Epub 2015 Apr 18. — View Citation
MacDonald NE; SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015 Aug 14;33(34):4161-4. doi: 10.1016/j.vaccine.2015.04.036. Epub 2015 Apr 17. — View Citation
Milinovich GJ, Williams GM, Clements AC, Hu W. Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infect Dis. 2014 Feb;14(2):160-8. doi: 10.1016/S1473-3099(13)70244-5. Epub 2013 Nov 28. Review. — View Citation
Pennebaker J, Boyd R, Jordan K, et al. The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin, 2015.
Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a Tool for Health Research: A Systematic Review. Am J Public Health. 2017 Jan;107(1):e1-e8. Epub 2016 Nov 17. Review. — View Citation
Stone JA, Can SH. Linguistic analysis of municipal twitter feeds: Factors influencing frequency and engagement. Gov Inf Q, 2020, 37(4): 101468.
Szilagyi PG, Thomas K, Shah MD, Vizueta N, Cui Y, Vangala S, Kapteyn A. National Trends in the US Public's Likelihood of Getting a COVID-19 Vaccine-April 1 to December 8, 2020. JAMA. 2020 Dec 29. doi: 10.1001/jama.2020.26419. [Epub ahead of print] — View Citation
Zhao N, Jiao D, Bai S, Zhu T. Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services. PLoS One. 2016 Jun 20;11(6):e0157947. doi: 10.1371/journal.pone.0157947. eCollection 2016. — View Citation
* Note: There are 14 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Changes in the prevalence of vaccine confidence and hesitancy | Vaccine confidence refers to the public's tweets expressing trust in the safety and effectiveness of vaccine, recognition of the vaccination necessity, and vaccine acceptance. Vaccine hesitancy means that the tweets express vaccine-related misinformation and rumors, worry about the safety and effectiveness of the vaccine, and vaccine rejection. The investigators will calculate the ratio of these two categories in all vaccine-related tweets as the prevalence of vaccine confidence and vaccine hesitancy. | Change from baseline prevalence of vaccine confidence and vaccine hesitancy at 1 year. | |
Secondary | Changes in the prevalence of machine-generated topics | Machine-generated topics refer to vaccine-related topics automatically generated through machine learning methods, such as political conspiracy, vaccine exemption, vaccine adverse events, and others. The investigators will calculate the ratio of tweets involved in each machine-generated topic in all vaccine-related tweets as the prevalence of machine-generated topics. | Change from baseline prevalence of machine-generated topics at 1 year. | |
Secondary | Changes in the public engagement on social media | Public engagement on social media is a comprehensive evaluation index to measure the transmit, reply, and like. The investigators will record the baseline and corresponding values after one year. | Change from baseline public engagement on social media at 1 year. |
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