@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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}."
}