Szczegóły publikacji
Opis bibliograficzny
$MDAM^3$: a misinformation detection and analysis framework for multitype multimodal media / Qingzheng Xu, Heming Du, Szymon ŁUKASIK, Tianqing Zhu, Sen Wang, Xin Yu // W: WWW'25 [Dokument elektroniczny] : proceedings of the ACM Web Conference 2025 : Sydney, Australia 28 April 2025 - 2 May 2025 / eds. Guodong Long, Michael Blumenstein, Yi Chang. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, cop. 2025. — e-ISBN: 979-8-4007-1274-6. — S. 5285-5296. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 5293–5294, Abstr.
Autorzy (6)
- Xu Qingzheng
- Du Heming
- AGHŁukasik Szymon
- Zhu Tianqing
- Wang Sen
- Yu Xin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160150 |
|---|---|
| Data dodania do BaDAP | 2025-06-26 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3696410.3714498 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | International World Wide Web Conference 2025 |
Abstract
Misinformation is a significant societal issue with potentially severe consequences. It appears in text, image, audio, and video modalities, encompassing various categories such as unimodal deception (fact-conflicting, AI-generated & offensive content) and cross-modal inconsistencies. However, current detection approaches often focus on text and image, overlooking the growing prevalence of misinformation in audio and video content. Moreover, these methods typically tend to address only one or two types of misinformation, failing to address all categories simultaneously. These detectors are also usually designed to make judgments without providing explanations, reducing transparency and limiting their broader applicability. To address these issues, we propose MDAM3, a Misinformation Detection and Analysis Framework for Multitype Multimodal Media. MDAM3 analyzes each input in internal detection and examines relationships across modalities to identify inconsistencies. It utilizes web resources and integrates Large Vision-Language Models (LVLMs) to deliver accurate detection results along with detailed analysis. To evaluate MDAM3, we curate MDAM3-DB, a specialized multitype multimodal misinformation dataset. A user study is conducted to explore MDAM3’s usability, interpretability, and effectiveness. We hope this research contributes to advancing misinformation detection methodologies and provides valuable insights for developing robust multimodal analysis tools.
