COVID-VTS: Fact Extraction and Verification on Short Video Platforms
EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
; : 178-188, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20238781
ABSTRACT
We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19-focused information from both the real world and machine generation. We propose, TwtrDetective, an effective model incorporating cross-media consistency checking to detect token-level malicious tampering in different modalities, and generate explanations. Due to the scarcity of training data, we also develop an efficient and scalable approach to automatically generate misleading video posts by event manipulation or adversarial matching. We investigate several state-of-the-art models and demonstrate the superiority of TwtrDetective. © 2023 Association for Computational Linguistics.
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Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Ensayo controlado aleatorizado
Idioma:
Inglés
Revista:
EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Año:
2023
Tipo del documento:
Artículo
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