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The report finds that text-based GenAI misinformation currently poses a higher persuasive risk than visual misinformation. It identifies preemptive debunking as the most consistent intervention for reducing perceived credibility, while content labeling is effective only when standardized. The analysis highlights a critical evidence gap, as existing research is heavily concentrated in English-speaking and high-income countries.

The report finds that textual GenAI misinformation is increasingly perceived as credible over time, while visual GenAI misinformation, such as deepfakes, shows a decline in perceived credibility. Preventive corrective information and content labeling are identified as effective countermeasures, though their impact varies by context.

The report finds that text-based generative AI misinformation currently poses a greater persuasive risk than visual misinformation. It identifies preventive corrective information as the most consistently effective intervention to reduce the perceived credibility of such content, while noting that content labeling is promising but highly dependent on consistent design and implementation. The authors highlight a critical evidence gap, as current research is heavily concentrated in English-language and high-income countries.

The report finds that generative AI-produced textual misinformation currently poses a higher persuasion risk than visual misinformation. It identifies preventive corrective information as the most consistently effective intervention for reducing the perceived credibility of such content, while content labeling shows promising but inconsistent results depending on design. The analysis highlights a significant global evidence gap, as most research is concentrated in high-income, English-speaking countries.

The report finds that text-based generative AI misinformation poses a higher persuasive risk than visual misinformation. It identifies a significant knowledge gap due to research focusing primarily on high-income, English-speaking countries. Among countermeasures, preemptive corrective information is the most consistent and effective intervention, while content labeling shows promising but inconsistent results depending on design and application.