Assessing Brigada Digital de Salud Audience Reach and Engagement: A Digital Community Health Worker Model to Address COVID-19 Misinformation in Spanish on Social Media.
Elizabeth Louise AndradeLorien C AbromsAnna I GonzálezCarla FavettoValeria GomezManuel Díaz-RamírezCésar PalaciosMark C EdbergPublished in: Vaccines (2023)
U.S. Spanish-speaking populations experienced gaps in timely COVID-19 information during the pandemic and disproportionate misinformation exposure. Brigada Digital de Salud was established to address these gaps with culturally tailored, Spanish-language COVID-19 information on social media. From 1 May 2021 to 30 April 2023, 495 Twitter, 275 Facebook, and 254 Instagram posts were published and amplified by 10 trained community health workers. A qualitative content analysis was performed to characterize the topics and formats of 251 posts. To assess reach and engagement, page analytics and advertising metrics for 287 posts were examined. Posts predominantly addressed vaccination (49.45%), infection risks (19.12%), and COVID-related scientific concepts (12.84%). Posts were educational (48.14%) and aimed to engage audiences (23.67%), promote resources (12.76%), and debunk misinformation (9.04%). Formats included images/text (55.40%), carousels (27.50%), and videos (17.10%). By 9 June 2023, 394 Facebook, 419 Instagram, and 228 Twitter followers included mainly women ages 24-54. Brigada Digital reached 386,910 people with 552,037 impressions and 96,868 engagements, including 11,292 likes, 15,240 comments/replies, 9718 shares/retweets, and 45,381 video play-throughs. The most engaging posts included videos with audio narration, healthcare providers, influencers, or music artists. This community-based model to engage Spanish-speaking audiences on social media with culturally aligned content to counter misinformation shows promise for addressing public health threats.
Keyphrases
- social media
- coronavirus disease
- sars cov
- health information
- public health
- healthcare
- respiratory syndrome coronavirus
- big data
- autism spectrum disorder
- polycystic ovary syndrome
- systematic review
- optical coherence tomography
- metabolic syndrome
- machine learning
- pregnant women
- resistance training
- artificial intelligence
- insulin resistance