Social Media-based Vaccine Confidence and Hesitancy Monitoring
Study Details
Study Description
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.
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Detailed Description
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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.
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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.
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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.
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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.
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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.
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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 Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
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| 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. |
Outcome Measures
Primary Outcome Measures
- Changes in the prevalence of vaccine confidence and hesitancy [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.
Secondary Outcome Measures
- Changes in the prevalence of machine-generated topics [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.
- Changes in the public engagement on social media [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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Tweets and weico posts related to vaccines
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Published in 2015-2022
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English tweets
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Tweets/posts from personal accounts.
Exclusion Criteria:
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Tweets/posts from news, organization accounts, or authenticated users
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Non English tweets.
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Fudan University
- Merck Sharp & Dohme LLC
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res, 2003, 3:993-1022.
- 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, 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.
- 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.
- 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.
- 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.
- 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.
- Stone JA, Can SH. Linguistic analysis of municipal twitter feeds: Factors influencing frequency and engagement. Gov Inf Q, 2020, 37(4): 101468.
- 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.
- ECT2112016948