{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:14:47Z","timestamp":1781367287003,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20474"],"award-info":[{"award-number":["U21A20474"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62166004"],"award-info":[{"award-number":["62166004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62366052"],"award-info":[{"award-number":["62366052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Natural Science Foundation","award":["2024JJA170142"],"award-info":[{"award-number":["2024JJA170142"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The widespread dissemination of multimodal disinformation, which combines inflammatory text with manipulated images, poses a severe threat to society. Existing detection methods typically process textual and visual features in isolation or perform simple fusion, failing to capture the sophisticated semantic inconsistencies commonly found in false information. To address this, we propose a novel framework: Emotion-Topic Injection and Consistency Detection Network (ETICD-Net). First, a large language model (LLM) extracts structured sentiment and topic-guided signals from news texts to provide rich semantic clues. Second, unlike previous approaches, this guided signal is injected into the feature extraction processes of both modalities: it enhances text features from BERT and modulates image features from ResNet, thereby generating sentiment-topic-aware feature representations. Additionally, this paper introduces a hierarchical consistency fusion module that explicitly evaluates semantic coherence among these enhanced features. It employs cross-modal attention mechanisms, enabling text to query image regions relevant to its statements, and calculates explicit dissimilarity metrics to quantify inconsistencies. Extensive experiments on the Weibo and Twitter benchmark datasets demonstrate that ETICD-Net outperforms or matches state-of-the-art methods, achieving accuracy and F1 scores of 90.6% and 91.5%, respectively.<\/jats:p>","DOI":"10.3390\/informatics12040129","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T10:46:18Z","timestamp":1764067578000},"page":"129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ETICD-Net: A Multimodal Fake News Detection Network via Emotion-Topic Injection and Consistency Modeling"],"prefix":"10.3390","volume":"12","author":[{"given":"Wenqian","family":"Shang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"},{"name":"School of Software, Xinjiang University, Urumqi 830000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinru","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0946-5237","authenticated-orcid":false,"given":"Tong","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830000, China"},{"name":"The Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830000, China"},{"name":"The Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17583","DOI":"10.1109\/ACCESS.2025.3530688","article-title":"Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning","volume":"13","author":"Plikynas","year":"2025","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TAFFC.2023.3295806","article-title":"Fake News, Real Emotions: Emotion Analysis of COVID-19 Infodemic in Weibo","volume":"15","author":"Wan","year":"2024","journal-title":"IEEE Trans. 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