Social Media-based Vaccine Confidence and Hesitancy Monitoring

June 28, 2022 updated by: Zhiyuan Hou, Fudan University

A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time

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.

Study Overview

Status

Withdrawn

Conditions

Detailed Description

  1. Collect and update social media data regarding vaccines The investigators will automatically collect all social media posts regarding vaccines in real time. Social media cohort database will be established and updated for all vaccines and Human Papilloma Virus(HPV) vaccine, respectively.
  2. Monitor vaccine confidence and hesitancy in real time: deep (supervised) machine learning models Deep learning model, a supervised machine learning technique, will be used to analyze text data on social media in real time according to the predefined vaccine confidence and hesitancy framework. The investigators will first manually annotate a subset of social media posts (20,000 posts) regarding vaccines. The initial manually-annotated posts are then used to train and evaluate deep learning models. Deep learning models with the best performance are selected and applied to classify all vaccine-related posts according to the vaccine confidence and hesitancy framework.
  3. Monitor emerging concerns and sentiment swings in real time to early warn vaccine-related risks or crises: topic (unsupervised) machine learning models and linguistic analysis There are some topics outside of the predefined vaccine confidence and hesitancy framework used in deep learning models, and new topics emerge in any time. Vaccine crisis would influence public sentiments. Monitoring emerging topics and sentiment swings will provide early warning of vaccine-related risks or crises. Use Topic Modeling, an unsupervised machine learning technique that can automatically classify text to representative topics in social media, to monitor emerging topics and concerns regarding vaccines.
  4. Assess public engagement on social media to inform effective health communication strategies: social media engagement analysis Besides posts data on social media, engagement data of posts are also available to be analyzed, including likes, comments, and shares of posts. The investigators will conduct social media engagement analysis to investigate public communication around vaccines online. This will guide the design of effective health communication strategies.
  5. Establish social media surveillance and analysis platform for vaccine confidence and crisis Through the steps above, the investigators will establish a social media surveillance and analysis platform for vaccine confidence and crisis. Time-series trends, geographic variation, and associated factors of the indicators produced above will be presented to monitor vaccine confidence in real time, early warn emerging risks or crises, and inform effective health communication strategies.
  6. Past research experience The investigators have conducted a series of relevant studies to analyze social media data using machine learning techniques during the COVID-19 epidemic, covering COVID-19 vaccine confidence and public response to COVID-19. These experiences make the current study feasible.

Study Type

Observational

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • ADULT
  • OLDER_ADULT
  • CHILD

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Changes in the prevalence of vaccine confidence and hesitancy
Time Frame: Change from baseline prevalence of vaccine confidence and vaccine hesitancy at 1 year.
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Changes in the prevalence of machine-generated topics
Time Frame: Change from baseline prevalence of machine-generated topics at 1 year.
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.
Changes in the public engagement on social media
Time Frame: Change from baseline public engagement on social media at 1 year.
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.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fudan University

Collaborators

Merck Sharp & Dohme LLC

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

March 1, 2022

Primary Completion (ACTUAL)

June 1, 2022

Study Completion (ACTUAL)

June 24, 2022

Study Registration Dates

First Submitted

March 21, 2022

First Submitted That Met QC Criteria

June 28, 2022

First Posted (ACTUAL)

July 5, 2022

Study Record Updates

Last Update Posted (ACTUAL)

July 5, 2022

Last Update Submitted That Met QC Criteria

June 28, 2022

Last Verified

June 1, 2022

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • ECT2112016948

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

According to the terms of the agreement, individual data can not be shared.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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