{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:54:49Z","timestamp":1781589289295,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MG021"],"award-info":[{"award-number":["ZR2021MG021"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016697","name":"Youth Innovation Technology Project of Higher School in Shandong Province","doi-asserted-by":"publisher","award":["2021RW030"],"award-info":[{"award-number":["2021RW030"]}],"id":[{"id":"10.13039\/100016697","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>On current social media platforms, multimodal fake news has permeated various fields. Multi-domain fake news detection has garnered significant attention in the academic community. Existing multi-domain methods primarily employ feature fusion techniques based on text\u2013image alignment, neglecting the extraction of conflicting information across modalities and failing to address the domain-dependent nature of cross-modal feature conflicts. To address this, we propose a Multi-Granularity Conflict and Domain-Guided Calibration for Multimodal Fake News Detection model (GC2MFND). This model captures conflicting features through the domain-aware multi-granularity conflict extraction module and mitigates feature suppression using the domain-guided multimodal feature calibration module. Finally, it combines domain-adaptive aggregation with multi-view evidence integration to achieve robust decision-making under supervised contrastive learning constraints. Under known domain conditions, the experimental results demonstrate that GC2MFND outperforms existing multi-domain baseline methods, achieving accuracy rates of 95.3%, 95.7%, and 81.2% on the Weibo, Weibo21, and FineFake datasets, respectively, representing improvements of 1.1%, 1.2%, and 1.4% over the corresponding multi-domain baselines.<\/jats:p>","DOI":"10.3390\/e28060672","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:46:28Z","timestamp":1781588788000},"page":"672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GC2MFND: Multi-Granularity Conflict and Domain-Guided Calibration for Multimodal Fake News Detection"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8058-0630","authenticated-orcid":false,"given":"Yanming","family":"Sun","sequence":"first","affiliation":[{"name":"School of Transportation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fujun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Library, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Zhang, Y., and Luo, J. (2016, January 12\u201317). News verification by exploiting conflicting social viewpoints in microblogs. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10382"},{"key":"ref_2","first-page":"679","article-title":"Fake news makes the news: Definitions and framing of fake news in mainstream media","volume":"20","author":"Boudana","year":"2026","journal-title":"J. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, Q., Liao, Y., Li, Z., and Liang, S. (2023). Multi-modal representation via contrastive learning with attention bottleneck fusion and attentive statistics features. Entropy, 25.","DOI":"10.3390\/e25101421"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alghamdi, J., Lin, Y., and Luo, S. (2024). Cross-domain fake news detection using a prompt-based approach. Future Internet, 16.","DOI":"10.3390\/fi16080286"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1080\/08838151.2026.2630874","article-title":"Fake News is Shared by \u201cThem\u201d Not \u201cUs\u201d on Social Media: Perceptual Gaps of Fake News Sharing and Affective Polarization","volume":"70","author":"Lu","year":"2026","journal-title":"J. Broadcast. Electron. Media"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Su, Y., and Zhao, X. (2025). Hierarchical Text-Guided Refinement Network for Multimodal Sentiment Analysis. Entropy, 27.","DOI":"10.3390\/e27080834"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hu, X., and Zhang, H. (2025). Invariant representation learning in multimedia recommendation with modality alignment and model fusion. Entropy, 27.","DOI":"10.3390\/e27010056"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104391","DOI":"10.1016\/j.ipm.2025.104391","article-title":"Emotion-semantic interaction network for fake news detection: Perspectives on question and non-question comment semantics","volume":"63","author":"Tan","year":"2026","journal-title":"Inf. Process. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s10796-022-10242-z","article-title":"Fake news on social media: The impact on society","volume":"26","author":"Olan","year":"2024","journal-title":"Inf. Syst. Front."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nan, Q., Cao, J., Zhu, Y., Wang, Y., and Li, J. (2021, January 1\u20135). MDFEND: Multi-domain Fake News Detection. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Gold Coast, QLD, Australia.","DOI":"10.1145\/3459637.3482139"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, W., Dang, Y., and Zhang, X. (2025). A Multimodal Semantic-Enhanced Attention Network for Fake News Detection. Entropy, 27.","DOI":"10.3390\/e27070746"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deng, B. (2025). Exploring Universal Domain Adaptation with CLIP Models: A Calibration Method. Entropy, 27.","DOI":"10.3390\/e27121213"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3778","DOI":"10.1109\/TKDE.2026.3677544","article-title":"Noise-Filtering Enhanced Graph Transformer for Robust Fake News Detection","volume":"38","author":"Zhu","year":"2026","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","unstructured":"Tong, Y., Lu, W., Zhao, Z., Lai, S., and Shi, T. (November, January 28). MMDFND: Multi-modal Multi-Domain Fake News Detection. Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne, VIC, Australia."},{"key":"ref_15","unstructured":"Lu, W., Tong, Y., and Ye, Z. (March, January 25). DAMMFND: Domain-Aware Multimodal Multi-view Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, T., Yu, E., Shao, Y., and Sun, J. (2025, January 16\u201322). Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection. Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2025\/883"},{"key":"ref_17","unstructured":"Yu, X., Sheng, Z., Lu, W., Luo, X., and Zhou, J. (March, January 25). RaCMC: Residual-aware compensation network with multi-granularity constraints for fake news detection. Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guan, W., Wen, H., Song, X., Yeh, C.H., Chang, X., and Nie, L. (2021, January 20\u201324). Multimodal Compatibility Modeling via Exploring the Consistent and Complementary Correlations. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475392"},{"key":"ref_19","unstructured":"Chen, R., Rong, Y., Guo, S., Han, J., Sun, F., Xu, T., and Huang, W. (2022). Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Z., Gao, Y., and Hu, X. (2022, January 10\u201314). Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation. Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal.","DOI":"10.1145\/3503161.3548114"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.ipm.2019.03.004","article-title":"An overview of online fake news: Characterization, detection, and discussion","volume":"57","author":"Zhang","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv, arXiv:1409.1556."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., and Gao, J. (2018, January 19\u201323). EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219903"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shao, Y., Sun, J., Zhang, T., Jiang, Y., Ma, J., and Li, J. (2022, January 27\u201330). Fake News Detection Based on Multi-Modal Classifier Ensemble. Proceedings of the 1st International Workshop on Multimedia AI against Disinformation, Newark, NJ, USA.","DOI":"10.1145\/3512732.3533583"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kutay, E., and Yener, A. (2025). Harnessing the Power of Pre-Trained Models for Efficient Semantic Communication of Text and Images. Entropy, 27.","DOI":"10.3390\/e27080813"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Singhal, S., Shah, R.R., Chakraborty, T., Kumaraguru, P., and Satoh, S. (2019, January 11\u201313). SpotFake: A Multi-modal Framework for Fake News Detection. Proceedings of the 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), Singapore.","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2022, January 18\u201324). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_28","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, Online."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, Y., Li, Z., Yao, R., Zhang, Y., and Wang, D. (May, January 28). Modality Interactive Mixture-of-Experts for Fake News Detection. Proceedings of the ACM on Web Conference 2025, Sydney, NSW, Australia.","DOI":"10.1145\/3696410.3714522"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, D., Zhang, P., Sui, J., Lv, Q., Tun, L., and Shang, L. (2022, January 25\u201329). Cross-modal Ambiguity Learning for Multimodal Fake News Detection. Proceedings of the ACM Web Conference 2022, Lyon, France.","DOI":"10.1145\/3485447.3511968"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, J., Yao, S., Wang, R., and Du, H. (2023). Tlfnd: A multimodal fusion model based on three-level feature matching distance for fake news detection. Entropy, 25.","DOI":"10.3390\/e25111533"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shen, L., Long, Y., Cai, X., Razzak, I., Chen, G., Liu, K., and Jameel, S. (2025, January 10\u201314). GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection. Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, Hannover, Germany.","DOI":"10.1145\/3701551.3703541"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lu, W., and Li, Y. (2026, January 20\u201327). From Blind Transfer to Wise Selection: Prototype-Driven Neighbor-Domain Adaptation for Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Singapore.","DOI":"10.1609\/aaai.v40i1.37049"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"18417","DOI":"10.1007\/s11227-023-05381-2","article-title":"KG-MFEND: An efficient knowledge graph-based model for multi-domain fake news detection","volume":"79","author":"Fu","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104700","DOI":"10.1016\/j.ipm.2026.104700","article-title":"Multimodal fusion with LLM content via hierarchical progressive transformer for explainable fake news detection","volume":"63","author":"Wang","year":"2026","journal-title":"Inf. Process. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TCE.2026.3656410","article-title":"Securing Consumer Applications Against AI-Driven Misinformation: A Cross-Domain Multimodal Approach","volume":"72","author":"Luo","year":"2026","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2021.07.077","article-title":"Knowledge augmented transformer for adversarial multidomain multiclassification multimodal fake news detection","volume":"462","author":"Song","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103807","DOI":"10.1016\/j.ipm.2024.103807","article-title":"Entity-centric multi-domain transformer for improving generalization in fake news detection","volume":"61","author":"Bazmi","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, T., Wang, D., Chen, H., Zeng, Z., Guo, W., Miao, C., and Cui, L. (2020, January 19\u201324). BDANN: BERT-Based Domain Adaptation Neural Network for Multi-Modal Fake News Detection. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206973"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, J., Feng, X., Gu, T., and Chang, L. (2024, January 13\u201316). Dual-Teacher De-Biasing Distillation Framework for Multi-Domain Fake News Detection. Proceedings of the 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, The Netherlands.","DOI":"10.1109\/ICDE60146.2024.00279"},{"key":"ref_41","first-page":"7178","article-title":"Memory-Guided Multi-View Multi-Domain Fake News Detection","volume":"35","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhao, Z., Shi, L., Kuang, Z., and Liu, Y. (2023). Collaborative mixture-of-experts model for multi-domain fake news detection. Electronics, 12.","DOI":"10.3390\/electronics12163440"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113633","DOI":"10.1016\/j.dss.2021.113633","article-title":"Improving fake news detection with domain-adversarial and graph-attention neural network","volume":"151","author":"Yuan","year":"2021","journal-title":"Decis. Support Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, K., Wang, S., and Diao, Z. (2025). DATTAMM: Domain-Aware Test-Time Adaptation for Multimodal Misinformation Detection. Appl. Sci., 15.","DOI":"10.3390\/app152111832"},{"key":"ref_45","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_47","unstructured":"Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D. (2021). Supervised Contrastive Learning. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"104253","DOI":"10.1016\/j.inffus.2026.104253","article-title":"FineFake: A knowledge-enriched dataset for fine-grained multi-domain fake news detection","volume":"132","author":"Zhou","year":"2026","journal-title":"Inf. Fusion"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102610","DOI":"10.1016\/j.ipm.2021.102610","article-title":"Detecting fake news by exploring the consistency of multimodal data","volume":"58","author":"Xue","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Guo, H., Zhang, Y., and Luo, J. (2017, January 23\u201327). Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123454"},{"key":"ref_51","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Qin, Z., Cheng, Y., Zhao, Z., Chen, Z., Metzler, D., and Qin, J. (2020, January 6\u201310). Multitask Mixture of Sequential Experts for User Activity Streams. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Online.","DOI":"10.1145\/3394486.3403359"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wu, L., Liu, P., and Zhang, Y. (2023, January 7\u201314). See how you read? multi-reading habits fusion reasoning for multi-modal fake news detection. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, Washington, DC, USA.","DOI":"10.1609\/aaai.v37i11.26609"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sun, J., Chen, C., Hou, C., Wu, Y., and Yuan, X. (May, January 28). Multimodal Taylor Series Network for Misinformation Detection. Proceedings of the ACM on Web Conference 2025, Sydney, NSW, Australia.","DOI":"10.1145\/3696410.3714719"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/6\/672\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:49:22Z","timestamp":1781588962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/6\/672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":54,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["e28060672"],"URL":"https:\/\/doi.org\/10.3390\/e28060672","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]}}}