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From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis

Paper · 2026 · Zhi Zeng, Jiaying Wu, Minnan Luo et al.

Summary

Research on misleading short videos usually reduces the task to a real-or-fake label, so it cannot show how or why a clip deceives viewers. The authors argue for step-by-step, justified analysis and contribute both a benchmark and a method. MisVideoQA contains 1,174 micro-videos and 5,235 question–answer pairs, produced with a mix of human and automatic annotation. It covers 12 video categories, 10 of which are deceptive (for example text, video or audio tampering, deepfakes, AI edits, event fabrication and splicing, and faulty logic). Its multi-turn questions span six dimensions, moving from perception and content reasoning to fine-grained attribution and then to emotion, intent and persuasion. MLLMs often produce fragile, poorly grounded rationales. To address this, the authors propose MisAgent, a multi-agent system modeled on Delphi-style consensus building. Its specialized agents handle background verification, event-level inspection and the merging of retrieved external evidence. Leading MLLMs perform poorly on MisVideoQA, while MisAgent improves both answer accuracy and explanation quality. This points toward auditable, evidence-based moderation tools.

Key points

  • MisVideoQA has 1,174 videos, 5,235 QAs, 12 categories (10 deceptive types) and six QA dimensions. Its sources include YouTube, Instagram and Bilibili, and 292 samples are reused from FMNV.
  • The deception taxonomy groups types into manipulation, AIGC, out-of-context reuse and biases. Examples include knowledge errors, exaggerated narration and offensive content.
  • In the ablations, dropping the textual or acoustic modality lowers performance. Removing multi-view evidence reasoning (w/o MER) causes a larger drop than removing the Content Analysis Agent (w/o MCU), which shows the value of external retrieval.
  • Plain step-by-step prompting can help perception questions but hurts attribution, since it tends to invent causal links. MisAgent leads on most fine-grained categories.
  • The dataset is released under CC BY-NC-SA 4.0, with user identities anonymized and 12 trained annotators. Data and code are at github.com/zzeng1998/MisVideoQA.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓National University of SingaporeSingapore · Singapore207 works · 39,576 citations🎓Xi'an Jiaotong UniversityXi'an · China42 works · 36,431 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksComputer Vision and Pattern Recognition · 3,486 worksSocial Sciences · 1,954 worksSociology and Political Science · 626 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,685 works

Cite

Cite as Zhi Zeng, Jiaying Wu, Minnan Luo, Di Zhang, Yifei Yang, Xiangzheng Kong, Herun Wan and Zihan Ma. 2026. From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1716

@inproceedings{zeng2026detection,
  title = {{From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis}},
  author = {Zhi Zeng, Jiaying Wu, Minnan Luo, Di Zhang, Yifei Yang, Xiangzheng Kong, Herun Wan and Zihan Ma},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1716},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1716}
}

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