The widespread spread of fake news on social media is a major challenge for trust in digital media, the credibility of information and crisis communication. In Saudi Arabia, one of the world’s most active markets for X (formerly Twitter) per capita, the platform functions as a vital virtual public sphere, but is highly vulnerable to health infodemics, socio-cultural polarization around Vision 2030 reforms, and automated bot campaigns. In this systematic literature review (PRISMA 2020 guidelines), we synthesize 22 primary studies (n = 22) from Scopus, Web of Science, and ERIC to explore misinformation domains, user trust dynamics, and platform governance limitations. Quality appraisal with the Mixed Methods Appraisal Tool (MMAT) showed a high methodological rigor in 81.8% (n = 18) of the included works. The results show that hybrid deep learning architectures (e.g., AraBERT with dual-branch CNNs) can achieve up to 99.2% detection accuracy and that user metadata features can improve credibility classification by 12.5%. Public trust is primarily based on Islamic ethics and official state communications. Operational bottlenecks include chronic shortages of annotated Saudi dialect corpora, barriers to newsroom AI integration, and outdated cybercrime laws on synthetic deepfakes. Therefore, a multi-layered governance framework that combines dialect-sensitive Explainable AI (XAI) models with adaptive legal regulations, newsroom capacity building, and socio-cultural trust dynamics is essential to effectively countering Arabic-language misinformation.
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