Social Media Strategy for COVID-19 Vaccination Equity

Overseen By Damon Centola

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Pennsylvania

Disqualifiers: No internet, Under 18, Outside US

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

4476 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment 'Independent Control, Online Social Network and Collective Intelligence Intervention' for COVID-19 vaccination equity?

Research shows that using social media influencers to share positive information about vaccines can effectively reach and engage communities, particularly among African Americans and Hispanics, suggesting this approach could help improve COVID-19 vaccination equity. Additionally, analyzing social data, like Google searches, can help predict vaccination participation and optimize vaccine distribution, which supports the potential effectiveness of using online social networks and collective intelligence in vaccination strategies. ^{1 2 3 4 5}

Is the social media strategy for COVID-19 vaccination equity safe for humans?

The research articles do not provide specific safety data for the social media strategy itself, as they focus on improving vaccine accessibility and equity rather than evaluating the safety of the strategy in humans. ^{6 7 8 9 10}

How does the social media strategy for COVID-19 vaccination equity differ from other treatments for COVID-19?

This treatment is unique because it focuses on using social media to influence public opinion and increase vaccination rates, rather than directly treating the virus itself. It leverages online platforms to spread awareness and address vaccine hesitancy, which is different from traditional medical treatments that involve medication or vaccines. ^{11 12 13 14 15}

What is the purpose of this trial?

Social technologies for health have already become essential means for providing underserved populations greater social connectedness and increased access to novel health information. However, these technologies have also had negative unintended consequences. The resulting digital divide in social technology takes many forms - from explicit racism that excludes African American and Latinx populations from the resources enjoyed by White and Asian members of online communities, to self-segregation for the purposes of identity preservation and community-building that unintentionally results in limited informational diversity in underserved communities. The result is an often unnoticed, but highly consequential compounding of inequities. This research seeks to use an online social network approach to address these challenges, in which the investigators demonstrate how reducing the online levels of network centralization and network homophily among African American community members directly increases their productive engagement with health-promoting information.

Research Team

Damon Centola, PhD

Principal Investigator

University of Pennsylvania

Eligibility Criteria

This trial is for adults over 18 living in the United States with internet access. It aims to improve health equity and COVID-19 vaccination rates among at-risk populations, particularly addressing challenges faced by African American and Latinx communities.

Inclusion Criteria

Living in the United States

Having internet access

Exclusion Criteria

I am under 18 years old.

Living outside of the United States

Having no internet access

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants engage in online social network activities to assess the impact of network structure on health information engagement

8 weeks

Participants engage asynchronously

Follow-up

Participants are monitored for changes in COVID-19 vaccination attitudes and beliefs

Immediate after intervention

Treatment Details

Interventions

• Independent Control
• Online Social Network and Collective Intelligence Intervention

Trial Overview The study tests an online social network intervention designed to reduce digital divide and increase engagement with health-promoting information. Participants will be compared to a control group without this intervention.

Participant Groups

Group I: Independent Control of Homogeneous Populations Experimental Treatment 1 Intervention

Independent control condition does not have online networks. Participants in this condition are not put into online networks. Participants only respond to questions by themselves. All participants in this condition share similar baseline demographic characteristics, attitudes, or behavioral choices.

Group II: Independent Control of Diverse Populations Experimental Treatment 1 Intervention

Independent control condition does not have online networks. Participants in this condition are not put into online networks. Participants only respond to questions by themselves. All participants in this condition have very different baseline demographic characteristics, attitudes, or behavioral choices.

Group III: Egalitarian Networks of Homogeneous Populations Experimental Treatment 1 Intervention

Egalitarian networks are characterized by equal connectivity for all participants in an online network for information exchange. Each network is consisted of 40 individual participants. All network participants in this condition share similar baseline demographic characteristics, attitudes, or behavioral choices.

Group IV: Egalitarian Networks of Diverse Populations Experimental Treatment 1 Intervention

Egalitarian networks are characterized by equal connectivity for all participants in an online network for information exchange. Each network is consisted of 40 individual participants. All network participants in this condition have very different baseline demographic characteristics, attitudes, or behavioral choices.

Group V: Centralized Networks of Homogeneous Populations Experimental Treatment 1 Intervention

Centralized networks have a small number of influential individuals, called "hubs," with connections to most other people. Centralized networks characterize situations in which most or all individuals are connected to, and seek advice from, a few well-connected "influencers." Each network is consisted of 40 individual participants. All network participants in this condition share similar baseline demographic characteristics, attitudes, or behavioral choices.

Group VI: Centralized Networks of Diverse Populations Experimental Treatment 1 Intervention

Centralized networks have a small number of influential individuals, called "hubs," with connections to most other people. Centralized networks characterize situations in which most or all individuals are connected to, and seek advice from, a few well-connected "influencers." Each network is consisted of 40 individual participants. All network participants in this condition have very different baseline demographic characteristics, attitudes, or behavioral choices.

Who Is Running the Clinical Trial?

University of Pennsylvania

Lead Sponsor

Trials

2,118

Recruited

45,270,000+

University of California, Davis

Collaborator

Trials

958

Recruited

4,816,000+

University of California, San Francisco

Collaborator

Trials

2,636

Recruited

19,080,000+

University of California, Berkeley

Collaborator

Trials

193

Recruited

716,000+

References

1. United States pubmed.ncbi.nlm.nih.gov

Epidemic Vulnerability Index for Effective Vaccine Distribution against Pandemic. [2022]

2. England pubmed.ncbi.nlm.nih.gov

Social media influencers can be used to deliver positive information about the flu vaccine: findings from a multi-year study. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

Catch the tweet to fight the flu: Using Twitter to promote flu shots on a college campus. [2023]

4. United States pubmed.ncbi.nlm.nih.gov

Analyzing Twitter for Community-Level Public Health Messaging. [2023]

5. United States pubmed.ncbi.nlm.nih.gov

Social Data: An Underutilized Metric for Determining Participation in COVID-19 Vaccinations. [2021]

6. United States pubmed.ncbi.nlm.nih.gov

Developing a Community-Oriented and Place-Based Strategy to Improve COVID-19 Vaccine Accessibility. [2023]

7. United States pubmed.ncbi.nlm.nih.gov

Vaccine hesitance and vaccine access in minority communities. [2021]

8. United States pubmed.ncbi.nlm.nih.gov

Targeting Equity in COVID-19 Vaccinations Using the "Evaluating Vulnerability and Equity" (EVE) Model. [2022]

9. United States pubmed.ncbi.nlm.nih.gov

Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response. [2023]

10. United States pubmed.ncbi.nlm.nih.gov

Quantifying inequities in COVID-19 vaccine distribution over time by social vulnerability, race and ethnicity, and location: A population-level analysis in St. Louis and Kansas City, Missouri. [2022]

11. Canada pubmed.ncbi.nlm.nih.gov

Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis. [2023]

12. Germany pubmed.ncbi.nlm.nih.gov

Dynamics of coronavirus pandemic: effects of community awareness and global information campaigns. [2021]

13. United States pubmed.ncbi.nlm.nih.gov

External intervention model with direct and indirect propagation behaviors on social media platforms. [2022]

14. United States pubmed.ncbi.nlm.nih.gov

What drives people to repost social media messages during the COVID-19 pandemic? Evidence from the Weibo news microblog. [2022]

15. England pubmed.ncbi.nlm.nih.gov

Shaping public opinion through the lens of agenda setting in rolling out COVID-19 vaccination program. [2021]