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

2026-04-15

AI Summary

This study addresses the growing challenge posed by generative AI in blurring the boundary between authentic and synthetic media, thereby amplifying the risks of multimodal disinformation. To systematically investigate the propagation dynamics and detection efficacy of AI-generated content, the authors introduce CONVEX, a novel dataset comprising 150,000 multimodal social media posts that uniquely integrates community annotations, user engagement metrics, and cross-modal synthetic content. Through large-scale data mining, specialized detectors, and vision-language models evaluated temporally, the research reveals that AI-generated content exhibits heightened virality yet relies more on passive user engagement and achieves consensus formation more rapidly. Critically, existing detection models suffer significant performance degradation as generative techniques evolve, underscoring the urgent need for adaptive, dynamic monitoring frameworks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Multi-modal Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

As generative AI advances, the distinction between authentic and synthetic media is increasingly blurred, challenging the integrity of online information. In this study, we present CONVEX, a large-scale dataset of multimodal misinformation involving miscaptioned, edited, and AI-generated visual content, comprising over 150K multimodal posts with associated notes and engagement metrics from X's Community Notes. We analyze how multimodal misinformation evolves in terms of virality, engagement, and consensus dynamics, with a focus on synthetic media. Our results show that while AI-generated content achieves disproportionate virality, its spread is driven primarily by passive engagement rather than active discourse. Despite slower initial reporting, AI-generated content reaches community consensus more quickly once flagged. Moreover, our evaluation of specialized detectors and vision-language models reveals a consistent decline in performance over time in distinguishing synthetic from authentic images as generative models evolve. These findings highlight the need for continuous monitoring and adaptive strategies in the rapidly evolving digital information environment.

Problem

• synthetic media
• multimodal misinformation
• AI-generated content
• virality
• detectability

Innovation

• synthetic media
• multimodal misinformation
• AI-generated content
• detectability
• virality