Social Media-based Vaccine Confidence and Hesitancy Monitoring

NCT05442762 | Withdrawn

• 1 other identifier: ECT2112016948
• Study Type: observational
• Target: N/A
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Social Medium; Data Collection; Patient Acceptance of Health Care; Immunization Programs; Vaccination Refusal; Information Dissemination; Machine Learning; Internet-Based Intervention

Keywords

vaccination hesitancy; vaccine confidence; social media; infodemiology; machine learning

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (2)

• Changes in the prevalence of machine-generated topics

  Change from baseline prevalence of machine-generated topics at 1 year.

• Changes in the public engagement on social media

  Change from baseline public engagement on social media at 1 year.

Study Arms (2)

Global Database of Vaccine Related Posts

Tweets in English from Twitter and posts from weico from 2015 to 2022 for all vaccines. The investigators only included posts from individual accounts and excluded those from news, organizational accounts, or verified users.

Global Database of HPV Vaccine Related Posts

Tweets in English from Twitter and posts from weico from 2015 to 2022 for HPV vaccine. The investigators only included posts from individual accounts and excluded those from news, organizational accounts, or verified users.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Vaccine-related posts on social media are the object of our research to assess public's vaccine confidence and hesitancy. Each post record comprises account name, profiles, contents, post time, the number of followers, and engagement data.

You may qualify if:

• Tweets and weico posts related to vaccines
• Published in 2015-2022
• English tweets
• Tweets/posts from personal accounts.

You may not qualify if:

• Tweets/posts from news, organization accounts, or authenticated users
• Non English tweets.

Sponsors & Collaborators

• Fudan University: lead
• Merck Sharp & Dohme LLC: collaborator

Related Publications (14)

• 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.

  PMID: 25896383 BACKGROUND

• 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.

  PMID: 24598724 BACKGROUND

• 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. doi: 10.2105/AJPH.2016.303512. Epub 2016 Nov 17.

  PMID: 27854532 BACKGROUND

• 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.

  PMID: 24290841 BACKGROUND

• Devlin J, Chang M-W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, 2018, arXiv:181004805.

  BACKGROUND

• 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.

  PMID: 25896384 BACKGROUND

• Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res, 2003, 3:993-1022.

  BACKGROUND

• Pennebaker J, Boyd R, Jordan K, et al. The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin, 2015.

  BACKGROUND

• 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.

  PMID: 27322382 BACKGROUND

• Stone JA, Can SH. Linguistic analysis of municipal twitter feeds: Factors influencing frequency and engagement. Gov Inf Q, 2020, 37(4): 101468.

  BACKGROUND

• 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: 10.1016/S0140-6736(20)31558-0. Epub 2020 Sep 10.

  PMID: 32919524 RESULT

• 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;325(4):396-8. doi: 10.1001/jama.2020.26419. Online ahead of print.

  PMID: 33372943 RESULT

• 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 Sep 13.

  PMID: 27658738 RESULT

• 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.

  PMID: 32287039 RESULT

MeSH Terms

Conditions

Patient Acceptance of Health Care; Vaccination Refusal; Vaccination Hesitancy

Condition Hierarchy (Ancestors)

Treatment Adherence and Compliance; Health Behavior; Behavior; Treatment Refusal

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: March 21, 2022
• First Posted: July 5, 2022
• Study Start: March 1, 2022
• Primary Completion: June 1, 2022
• Study Completion: June 24, 2022
• Last Updated: July 5, 2022

Record last verified: 2022-06

Data Sharing

• IPD Sharing: Will not share