@inproceedings{vatndal-setty-2025-shortcheck,
title = "{S}hort{C}heck: Checkworthiness Detection of Multilingual {S}hort{-}{F}orm Videos",
author = "Vatndal, Henrik and
Setty, Vinay",
editor = "Liu, Xuebo and
Purwarianti, Ayu",
booktitle = "Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-demo.9/",
pages = "77--85",
ISBN = "979-8-89176-301-2",
abstract = "Short-form video platforms like TikTok present unique challenges for misinformation detection due to their multimodal, dynamic, and noisy content. We present ShortCheck, a modular, inference-only pipeline with a user-friendly pipeline that automatically identifies checkworthy short-form videos to help human fact-checkers. The system integrates speech transcription, OCR, object and deepfake detection, video-to-text summarization, and claim verification. ShortCheck is validated by evaluating it on two manually annotated datasets with TikTok videos in a multilingual setting. The pipeline achieves promising results with F1-weighted score over 70{\%}. The demo can be accessed live at \url{http://shortcheck.factiverse.ai}."
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<abstract>Short-form video platforms like TikTok present unique challenges for misinformation detection due to their multimodal, dynamic, and noisy content. We present ShortCheck, a modular, inference-only pipeline with a user-friendly pipeline that automatically identifies checkworthy short-form videos to help human fact-checkers. The system integrates speech transcription, OCR, object and deepfake detection, video-to-text summarization, and claim verification. ShortCheck is validated by evaluating it on two manually annotated datasets with TikTok videos in a multilingual setting. The pipeline achieves promising results with F1-weighted score over 70%. The demo can be accessed live at http://shortcheck.factiverse.ai.</abstract>
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%0 Conference Proceedings
%T ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videos
%A Vatndal, Henrik
%A Setty, Vinay
%Y Liu, Xuebo
%Y Purwarianti, Ayu
%S Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-301-2
%F vatndal-setty-2025-shortcheck
%X Short-form video platforms like TikTok present unique challenges for misinformation detection due to their multimodal, dynamic, and noisy content. We present ShortCheck, a modular, inference-only pipeline with a user-friendly pipeline that automatically identifies checkworthy short-form videos to help human fact-checkers. The system integrates speech transcription, OCR, object and deepfake detection, video-to-text summarization, and claim verification. ShortCheck is validated by evaluating it on two manually annotated datasets with TikTok videos in a multilingual setting. The pipeline achieves promising results with F1-weighted score over 70%. The demo can be accessed live at http://shortcheck.factiverse.ai.
%U https://aclanthology.org/2025.ijcnlp-demo.9/
%P 77-85
Markdown (Informal)
[ShortCheck: Checkworthiness Detection of Multilingual Short‐Form Videos](https://aclanthology.org/2025.ijcnlp-demo.9/) (Vatndal & Setty, IJCNLP 2025)
ACL
- Henrik Vatndal and Vinay Setty. 2025. ShortCheck: Checkworthiness Detection of Multilingual Short‐Form Videos. In Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations, pages 77–85, Mumbai, India. Association for Computational Linguistics.