Mapping Information Quality on Social Media

Background of the study: Social media information quality poses a cross-domain governance challenge, escalating misinformation across social, healthcare, economic, and political sectors, while fragmented disciplinary analyses hinder consensus on digital literacy and AI-based interventions. Purpose: This study produces a comprehensive Scoping Review and Bibliometric Analyses (ScoRBA) of information quality in social media by systematically mapping the progression of the research themes and methodological paths across the field. Method: Going through 3,018 Scopus-indexed records (2015–2024) for bibliometric keyword co-occurrence mapping and 662 full-text eligible articles for thematic synthesis using the PAGER framework (Patterns, Advances, Gaps, Evidence for practice, and Research recommendations), five structurally stable thematic clusters were identified: information quality, trust, and acceptance; platform credibility and quality evaluation; COVID-19 public-health communication; information disorder and media literacy; and AI/NLP-based detection and sentiment analysis. Findings: These clusters confirm that failures in information quality are not domain-specific but organizationally cross-domain, covering computational, behavioural, and governance dimensions. Conclusion: The synthesis found that governance initiatives, digital literacy programs, and AI-based interventions are most effective in providing an integrated multidisciplinary evidence base for future research, platform governance, and cross-domain intervention design.