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.
Figures & tables
Figure 1: Example of an AI-generated image post on X, with the Community Note and external sources used to verify it.
Figure 2: Monthly Community Notes volume for both modalities and releases of popular generative AI tools.
Figure 3: Virality Share across both modalities.
Figure 4: Monthly Engagement Index for both modalities.
Image
Video
Type
Notes / Tweet
Helpful (%)
Consensus Probability
First Note (h)
Notes to Consensus
Notes / Tweet
Helpful (%)
Consensus Probability
First Note (h)
Notes to Consensus
Miscaptioned
1.89
66.8
0.284
9.41
1.47
1.85
71.1
0.295
9.06
1.41
Edited
1.85
70.5
0.304
8.47
1.42
1.83
71.2
0.299
9.76
1.40
AI-Generated
1.78
69.4
0.305
11.69
1.41
1.61
81.4
0.362
11.30
1.26
Table 1: Consensus metrics across misinformation categories for image and video subsets.
Image
Video
Type
Model
General
Any
Model
General
Any
Miscaptioned
0.94
1.58
2.48
0.98
1.72
2.68
Edited
1.35
1.47
2.80
0.92
1.63
2.53
AI-generated
9.47
56.75
60.58
12.25
47.07
53.04
Table 2: AI-related mentions in Community Notes. Values represent the percentage of notes containing general references to AI or to specific a model .
Figure 5: Temporal performance of AI-image detection systems from 2023 to 2025. Panels show TPR, FPR, and Accuracy across six-month evaluation periods. TPR declines consistently across models, while FPR remains relatively stable, suggesting that performance degradation is driven by reduced sensitivity to newer AI-generated images.
Model
TPR ↑
FPR ↓
Precision ↑
F1 ↑
Accuracy ↑
SPAI
41.48
24.66
62.90
49.99
58.34
RINE
52.75
30.54
63.75
57.73
61.03
BFree
52.72
27.81
65.65
58.47
62.42
Gemma 3 27B
70.21
31.00
69.51
69.86
69.60
Grok 4.1 Fast
52.28
21.16
71.35
60.35
65.51
GPT-5-mini
55.00
15.06
78.64
64.73
69.91
Table 3: Overall benchmark performance on the image dataset. Bold denotes best values and underlined the second-best.
Figure 6: Examples of AI-generated images with corresponding predictions from SIDs and VLMs.