CONVEX Multimodal Misinformation Dataset
Twitter (X)Ai agentsContent moderationEngagementFact-checkingImages & visual mediaMisinformation A X (Twitter) with Community Notes annotations dataset of 152,266 multimodal posts (21,845 AI-generated, 104,915 miscaptioned), collected by Chrysidis, Papadopoulos & Papadopoulos via X Community Notes (2026). Each record contains tweet ID, reply/favorite/view/retweet counts, Community Notes text and timestamp, and misinformation type label (miscaptioned / ai_generated / other). Large-scale dataset of 152,266 multimodal X posts with Community Notes — tracking the rise of AI-generated misinformation with 21,845 confirmed AI-generated posts. Includes engagement metrics showing that AI-generated content achieves disproportionate virality despite slower initial reporting.. Suitable for AI-generated content detection, misinformation classification, Community Notes effectiveness, multimodal misinformation analysis, virality and engagement modeling, temporal degradation of detectors.
Cite this dataset at
convex.echo.directory
Analytics
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Sampled: text metrics from 50K of 152.2K records; topic model from 20K.
Record
- Catalog key
convex- Authors
- Chrysidis, Zacharias; Papadopoulos, Stefanos-Iordanis; Papadopoulos, Symeon
- Year
- 2026
- Source paper
- The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation
- DOI / arXiv
- 10.48550/arXiv.2604.15372
- License
- Academic research use — see repository (content subject to X Terms of Service) (LicenseRef-Research-Only)
- Records
- 152,266
- Nodes are
- unknown
- Edges are
- unknown
- Text corpus
- Community Notes explanation text
- Languages
- en
- Collected
- 2021-01 to 2025-10 (2020s)
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://github.com/zachos99/convex-dataset link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 61.8 MB across 2 files. Checksums in MANIFEST.json.
Citations
The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation
Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos
Journal Article · arXiv.org · 2026
Cited by
- Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations
Paschalis Giakoumoglou, Manos Schinas et al. · 2026 · cited 0× - Evidence Triangulation for Multimodal Fact-Checking in the Wild
Stefanos-Iordanis Papadopoulos, Zacharias Chrysidis et al. · arXiv.org · 2026 · cited 0×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@misc{https://doi.org/10.48550/arxiv.2604.15372,
doi = {10.48550/ARXIV.2604.15372},
url = {https://arxiv.org/abs/2604.15372},
author = {Chrysidis, Zacharias and Papadopoulos, Stefanos-Iordanis and Papadopoulos, Symeon},
keywords = {Cryptography and Security (cs.CR), Artificial Intelligence (cs.AI), Multimedia (cs.MM), FOS: Computer and information sciences},
title = {The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation},
publisher = {arXiv},
year = {2026},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Chrysidis, Z., Papadopoulos, S.-I., & Papadopoulos, S. (2026). CONVEX Multimodal Misinformation Dataset [Data set]. https://doi.org/10.48550/arXiv.2604.15372
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Chrysidis, Zacharias, et al. CONVEX Multimodal Misinformation Dataset. 2026, doi.org/10.48550/arXiv.2604.15372.