Beyond Social Signals: Rethinking Misinformation Detection Architectures for Sub-Saharan African Health Contexts

Background: Health misinformation poses significant public health risks in Sub-Saharan Africa (SSA), yet automated detection systems developed for high-resource contexts may be ineffective in resource-constrained settings characterized by limited connectivity, linguistic diversity, and sparse social engagement signals. Objective: This systematic review investigated: (1) socio-technical barriers to implementing digital health misinformation detection in SSA; (2) the impact of social engagement signal scarcity on detection model reliability; and (3) the adequacy of existing digital health frameworks for addressing connectivity and language diversity. Methods: Following PRISMA guidelines, we searched SciSpace, Google Scholar, SCOPUS, and PubMed (2020-2025), identifying 11 studies for synthesis. Quality assessment employed a modified Mixed Methods Appraisal Tool (MMAT). Data extraction utilized a People-Process-Technology framework for barrier analysis. Results: All seven core studies demonstrated high methodological quality. Resource constraints emerged as the dominant barrier (86% of studies). Knowledge-guided detection architectures (F1: 0.85-0.93) outperformed social-signal-dependent approaches in low-engagement contexts. Only 14% of studies addressed low-resource African languages; none implemented SMS-based or offline detection systems despite widespread SSA connectivity constraints. Conclusions: Effective misinformation detection in SSA requires fundamental reconceptualization prioritizing knowledge-guided architectures, participatory design, modular interoperability, and hybrid human-AI workflows adapted to connectivity heterogeneity and linguistic diversity.