ReviewCureus2026
Artificial Intelligence-Driven Social Media Analysis of Vaccine Hesitancy, Confidence, and Trust in Africa: A Scoping Review.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Africa, the spread of vaccine hesitancy is associated with rapidly evolving digital narratives, but there is limited evidence on how artificial intelligence (AI) and natural language processing (NLP) have been used to study these narratives and the associated dynamics of confidence and trust. This scoping review mapped peer-reviewed primary studies applying AI, machine learning, and NLP to vaccine-related digital text in African settings. The review documented the data sources, analytical methods, validation, and stated policy or practice relevance of these studies. Using a Population-Concept-Context (PCC)-aligned search of PubMed/MEDLINE and Scopus, and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, 456 records were identified. After deduplication and dual independent screening, 13 studies were included. The included studies spanned North, West, East, and South Africa, with most analyses based on Twitter/X and supplemented by data from Facebook, YouTube, online news comments, and multi-platform social listening tools. Methodologically, the studies could be categorized as follows: (i) sentiment and topic modeling to track hesitancy, confidence, and trust-related attitude dynamics and risk signals during rollout; (ii) supervised classification for misinformation or fake news detection, including work on African languages and dialects; and (iii) operational social listening workflows that translate signals into response recommendations. Automation enabled near real-time monitoring and geographic or temporal segmentation across studies, but validation was commonly internal, and ethical transparency (privacy, consent, and data governance) was inconsistently reported. The potential of African AI-enabled infoveillance for targeted vaccine communication shows potential. However, there is a need for stronger context-sensitive model evaluation, multilingual coverage, and clearer integration pathways into decision-making.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.