The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation

arXiv:2604.15372 · cs.CR, cs.AI, cs.MM · Submitted 2026-04-15 · Read on arXiv

cs.CR, cs.AI, cs.MM

Submitted: 2026-04-15

Updated: 2026-04-15

Code: https://github.com/zachos99/convex-dataset

Terminology

• Generative Model — 8× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Generative Models — 7× in this paper · explained in 2 episodes
• Generative AI — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• True Positive Rate — 2× in this paper
• Commentary — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Community Moderation and the New Epistemology of Fact Checking on Social Media
• AMMeBa: A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wild
• Scaling Human Judgment in Community Notes with LLMs
• Detecting Multimedia Generated by Large AI Models: A Survey
• From Birdwatch to Community Notes, from Twitter to X: four years of community-based content moderation
• Timeliness, Consensus, and Composition of the Crowd: Community Notes on X
• Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation

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