{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T22:09:10Z","timestamp":1765577350288,"version":"3.48.0"},"reference-count":34,"publisher":"IGI Global","issue":"1","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<p>With the rise of fake news as a societal threat, misinformation detection has become crucial in natural language processing. Traditional methods struggle with inadequate unimodal feature extraction, weak text-image fusion, and limited integration of user context. To address these issues, we suggest an intelligent Fake News Detection Leveraging Semantic and Context-Driven Analysis. Our model extracts text and image features via DeBERTa and CLIP-ViT, while a collaborative attention module enhances cross-modal interactions. Additionally, a graph convolutional network (GCN) captures user dissemination behaviors and social influence within the Semantic Web. By integrating structured user knowledge and multimodal content, the model constructs a holistic, context-aware news representation. Experimental results show that IFN-SC achieves ACC scores of 0.943, 0.963, and 0.911 on Weibo, Twitter, and GossipCop, outperforming state-of-the-art methods and demonstrating the effectiveness of Semantic Web-enhanced multimodal fusion in fake news detection.<\/p>","DOI":"10.4018\/ijswis.378676","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T03:30:46Z","timestamp":1748662246000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Fake News Detection Leveraging Semantic and Context-Driven Analysis"],"prefix":"10.4018","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8171-1463","authenticated-orcid":true,"given":"Dongxiu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Guangxi University of Science and Technology, Liuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuxi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huaqiao University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"IJSWIS.378676-0","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-023-01028-5"},{"key":"IJSWIS.378676-1","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.326120"},{"key":"IJSWIS.378676-2","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-017-0143-x"},{"key":"IJSWIS.378676-3","doi-asserted-by":"crossref","unstructured":"Cui, W., Zhang, X., & Shang, M. (2024). Multi-modality frequency-aware cross attention network for fake news detection. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-23.","DOI":"10.3233\/JIFS-233193"},{"key":"IJSWIS.378676-4","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN60899.2024.10651148"},{"key":"IJSWIS.378676-5","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2023.1159063"},{"key":"IJSWIS.378676-6","unstructured":"Hartl, P., & Kruschwitz, U. (2022). Applying automatic text summarization for fake news detection. arxiv preprint arxiv:2204.01841."},{"key":"IJSWIS.378676-7","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW60847.2023.00022"},{"key":"IJSWIS.378676-8","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123454"},{"key":"IJSWIS.378676-9","doi-asserted-by":"publisher","DOI":"10.3390\/su12239904"},{"key":"IJSWIS.378676-10","unstructured":"Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arxiv preprint arxiv:1609.02907."},{"key":"IJSWIS.378676-11","doi-asserted-by":"publisher","DOI":"10.1080\/23808985.2021.1976070"},{"key":"IJSWIS.378676-12","doi-asserted-by":"crossref","unstructured":"Li, Y., Ji, K., Ma, K., Chen, Z., Zhou, J., & Wu, J. (2022). Fake news detection based on the correlation extension of multimodal information. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM)Joint International Conference on Web and Big Data (pp. 443-450). Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-25158-0_36"},{"key":"IJSWIS.378676-13","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.350095"},{"key":"IJSWIS.378676-14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3392409"},{"key":"IJSWIS.378676-15","doi-asserted-by":"crossref","unstructured":"Lv, H., Yang, W., Wei, F., Peng, J., & Geng, H. (2024). 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