{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:45Z","timestamp":1761176265804,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Rumor detection is to identify and verify the authenticity of information to distinguish between true and false statements. For various types of rumors on social media, there is currently a lack of methods that effectively handle either unimodal or multimodal information simultaneously. Additionally, previous methods also suffered from the lack of images, the inconsistencies between the textual and visual modalities, and those misleading images. To address the above issues, we propose a Universal Rumor Detection Method (URDM), which introduces adversarial training using Diffusion and CLIP to enforce modality consistency and adopts Large Language Models (LLMs) and CoT (Chain of Thought) to generate external knowledge. Specifically, to address the issues of the missing modality and the inconsistencies between text and images, we introduce a random time step fine-tuning method for the Diffusion model to improve CLIP\u2019s ability to judge text-image consistency, and enhance Diffusion\u2019s accuracy in generating images based on text. Concurrently, we incorporated the CoT to serve as an extension of external knowledge to address the issue of misleading images. The experimental results on two popular datasets demonstrate that our proposed URDM outperforms the state-of-the-art baselines.<\/jats:p>","DOI":"10.3233\/faia251294","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:57:24Z","timestamp":1761127044000},"source":"Crossref","is-referenced-by-count":0,"title":["Universal Rumor Detection on Modality Consistency and External Knowledge"],"prefix":"10.3233","author":[{"given":"Haibing","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peifeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaoming","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251294","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:57:25Z","timestamp":1761127045000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251294","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}