{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:55:57Z","timestamp":1776682557655,"version":"3.51.2"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00530-025-01715-7","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T18:58:46Z","timestamp":1739905126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["CCGN: consistency contrastive-learning graph network for multi-modal fake news detection"],"prefix":"10.1007","volume":"31","author":[{"given":"ShaoDong","family":"Cui","sequence":"first","affiliation":[]},{"given":"Kaibo","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Hiroyuki","family":"Shinnou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"1715_CR1","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ins.2019.05.035","volume":"497","author":"A Bondielli","year":"2019","unstructured":"Bondielli, A., Marcelloni, F.: A survey on fake news and rumour detection techniques. Inf. Sci. 497, 38\u201355 (2019). https:\/\/doi.org\/10.1016\/j.ins.2019.05.035","journal-title":"Inf. Sci."},{"key":"1715_CR2","doi-asserted-by":"publisher","unstructured":"Singhal, S., Shah, RR., Chakraborty, T., Kumaraguru, P., Satoh, S.: Spotfake: a multi-modal framework for fake news detection. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 39\u201347 (2019) https:\/\/doi.org\/10.1109\/BigMM.2019.00-44","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"1715_CR3","doi-asserted-by":"publisher","unstructured":"Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., Gao, J.: EANN: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery, New York, NY, USA, KDD \u201918, pp. 849\u2013857 (2018) https:\/\/doi.org\/10.1145\/3219819.3219903","DOI":"10.1145\/3219819.3219903"},{"key":"1715_CR4","doi-asserted-by":"publisher","unstructured":"Zhang, H., Fang, Q., Qian, S., Xu, C.: Multi-modal knowledge-aware event memory network for social media rumor detection. In: Proceedings of the 27th ACM International Conference on Multimedia, Association for Computing Machinery, New York, NY, USA, MM \u201919, pp. 1942\u20131951 (2019) https:\/\/doi.org\/10.1145\/3343031.3350850,","DOI":"10.1145\/3343031.3350850"},{"key":"1715_CR5","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Guo, H., Zhang, Y., Luo, J.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on Multimedia, pp 795\u2013816 (2017)","DOI":"10.1145\/3123266.3123454"},{"key":"1715_CR6","doi-asserted-by":"crossref","unstructured":"Zhou, X., Wu, J., Zafarani, R.: SAFE: similarity-aware multi-modal fake news detection. CoRR https:\/\/arxiv.org\/abs\/2003.04981 (2020)","DOI":"10.1007\/978-3-030-47436-2_27"},{"key":"1715_CR7","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.procs.2020.01.072","volume":"165","author":"P Bahad","year":"2019","unstructured":"Bahad, P., Saxena, P., Kamal, R.: Fake news detection using bi-directional LSTM-recurrent neural network. Procedia Comput. Sci. 165, 74\u201382 (2019). https:\/\/doi.org\/10.1016\/j.procs.2020.01.072","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"1715_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2020.100007","volume":"1","author":"JA Nasir","year":"2021","unstructured":"Nasir, J.A., Khan, O.S., Varlamis, I.: Fake news detection: a hybrid CNN-RNN based deep learning approach. Int. J. Inf. Manag. Data Insights 1(1), 100007 (2021). https:\/\/doi.org\/10.1016\/j.jjimei.2020.100007","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"1715_CR9","doi-asserted-by":"publisher","first-page":"106907","DOI":"10.1109\/ACCESS.2021.3100245","volume":"9","author":"S Ni","year":"2021","unstructured":"Ni, S., Li, J., Kao, H.Y.: Mvan: multi-view attention networks for fake news detection on social media. IEEE Access 9, 106907\u2013106917 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3100245","journal-title":"IEEE Access"},{"key":"1715_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107600","volume":"110","author":"TE Trueman","year":"2021","unstructured":"Trueman, T.E., Ashok Kumar, J., Narayanasamy, P., Vidya, J.: Attention-based C-BILSTM for fake news detection. Appl. Soft Comput. 110, 107600 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107600","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"1715_CR11","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s41060-021-00302-z","volume":"13","author":"S Raza","year":"2022","unstructured":"Raza, S., Ding, C.: Fake news detection based on news content and social contexts: a transformer-based approach. Int. J. Data Sci. Anal. 13(4), 335\u2013362 (2022)","journal-title":"Int. J. Data Sci. Anal."},{"key":"1715_CR12","unstructured":"Gundapu, S., Mamidi, R.: Transformer based automatic covid-19 fake news detection system. arXiv preprint arXiv:2101.00180 (2021)"},{"key":"1715_CR13","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.1109\/TCSS.2023.3298480","volume":"11","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Zhang, Q., Ding, F., Zhu, X., Yu, K.: A novel fake news detection model for context of mixed languages through multiscale transformer. IEEE Trans. Comput. Soc. Syst. 11, 5079\u20135089 (2023). https:\/\/doi.org\/10.1109\/TCSS.2023.3298480","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"1715_CR14","unstructured":"Li, T., Sun, Y., Hsu, Sl., Li, Y., Wong, R. C. W.: Fake news detection with heterogeneous transformer. arXiv preprint arXiv:2205.03100 (2022)"},{"key":"1715_CR15","doi-asserted-by":"publisher","unstructured":"Mahmud, FB., Rayhan, MMS., Shuvo, MH., Sadia, I., Morol, M.: A comparative analysis of graph neural networks and commonly used machine learning algorithms on fake news detection. In: 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), pp. 97\u2013102 (2022) https:\/\/doi.org\/10.1109\/CDMA54072.2022.00021","DOI":"10.1109\/CDMA54072.2022.00021"},{"key":"1715_CR16","doi-asserted-by":"publisher","unstructured":"Xu, W., Wu, J., Liu, Q., Wu, S., Wang, L.: Evidence-aware fake news detection with graph neural networks. In: Proceedings of the ACM Web Conference 2022, Association for Computing Machinery, New York, NY, USA, WWW \u201922, pp. 2501\u20132510 (2022) https:\/\/doi.org\/10.1145\/3485447.3512122","DOI":"10.1145\/3485447.3512122"},{"key":"1715_CR17","doi-asserted-by":"publisher","unstructured":"Hu, L., Yang, T., Zhang, L., Zhong, W., Tang, D., Shi, C., Duan, N., Zhou, M.: Compare to the knowledge: Graph neural fake news detection with external knowledge. In: Zong C, Xia F, Li W, Navigli R (eds) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, pp 754\u2013763 (2021) https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.62,","DOI":"10.18653\/v1\/2021.acl-long.62"},{"issue":"5","key":"1715_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103807","volume":"61","author":"P Bazmi","year":"2024","unstructured":"Bazmi, P., Asadpour, M., Shakery, A., Maazallahi, A.: Entity-centric multi-domain transformer for improving generalization in fake news detection. Inf. Process. Manag. 61(5), 103807 (2024)","journal-title":"Inf. Process. Manag."},{"key":"1715_CR19","doi-asserted-by":"publisher","unstructured":"Khattar, D., Goud, JS., Gupta, M., Varma, V.: Mvae: Multimodal variational autoencoder for fake news detection. In: The World Wide Web Conference, Association for Computing Machinery, New York, NY, USA, WWW \u201919, pp. 2915\u20132921 (2019) https:\/\/doi.org\/10.1145\/3308558.3313552","DOI":"10.1145\/3308558.3313552"},{"issue":"5","key":"1715_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102610","volume":"58","author":"J Xue","year":"2021","unstructured":"Xue, J., Wang, Y., Tian, Y., Li, Y., Shi, L., Wei, L.: Detecting fake news by exploring the consistency of multimodal data. Inf. Process. Manag. 58(5), 102610 (2021). https:\/\/doi.org\/10.1016\/j.ipm.2021.102610","journal-title":"Inf. Process. Manag."},{"issue":"11","key":"1715_CR21","doi-asserted-by":"publisher","first-page":"11141","DOI":"10.1109\/TKDE.2022.3231338","volume":"35","author":"L Hu","year":"2022","unstructured":"Hu, L., Chen, Z., Zhao, Z., Yin, J., Nie, L.: Causal inference for leveraging image-text matching bias in multi-modal fake news detection. IEEE Trans. Knowl. Data Eng. 35(11), 11141\u201311152 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1715_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120310","volume":"664","author":"L Hu","year":"2024","unstructured":"Hu, L., Zhao, Z., Qi, W., Song, X., Nie, L.: Multimodal matching-aware co-attention networks with mutual knowledge distillation for fake news detection. Inf. Sci. 664, 120310 (2024). https:\/\/doi.org\/10.1016\/j.ins.2024.120310","journal-title":"Inf. Sci."},{"key":"1715_CR23","doi-asserted-by":"publisher","unstructured":"Chen, Y., Li, D., Zhang, P., Sui, J., Lv, Q., Tun, L., Shang, L.: Cross-modal ambiguity learning for multimodal fake news detection. In: Proceedings of the ACM Web Conference 2022, Association for Computing Machinery, New York, NY, USA, WWW \u201922, p 2897\u20132905 (2022) https:\/\/doi.org\/10.1145\/3485447.3511968,","DOI":"10.1145\/3485447.3511968"},{"key":"1715_CR24","doi-asserted-by":"publisher","unstructured":"Li, J., Bin, Y., Zou, J., Wei, J., Wang, G., Yang, Y.: Cross-modal consistency learning with fine-grained fusion network for multimodal fake news detection. In: Proceedings of the 5th ACM International Conference on Multimedia in Asia, Association for Computing Machinery, New York, NY, USA, MMAsia \u201923 (2024) https:\/\/doi.org\/10.1145\/3595916.3626397,","DOI":"10.1145\/3595916.3626397"},{"issue":"1","key":"1715_CR25","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1715_CR26","unstructured":"Kipf, TN., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"1715_CR27","first-page":"8291","volume":"35","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Guo, J., Tang, Y., Wu, E.: Vision GNN: an image is worth graph of nodes. Adv. Neural Inf. Process. Syst. 35, 8291\u20138303 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1715_CR28","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, SX., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"1715_CR29","doi-asserted-by":"publisher","unstructured":"Jin, Z., Cao, J., Guo, H., Zhang, Y., Luo, J.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM International Conference on Multimedia, Association for Computing Machinery, New York, NY, USA, MM \u201917, pp. 795\u2013816 (2017) https:\/\/doi.org\/10.1145\/3123266.3123454,","DOI":"10.1145\/3123266.3123454"},{"key":"1715_CR30","unstructured":"Boididou, C., Andreadou, K., Papadopoulos, S., Dang\u00a0Nguyen, DT., Boato, G., Riegler, M., Kompatsiaris, Y., et\u00a0al.: Verifying multimedia use at mediaeval 2015. In: MediaEval 2015, vol 1436, CEUR-WS (2015)"},{"key":"1715_CR31","unstructured":"Devlin, J.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"1715_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1715_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115412","volume":"184","author":"R Kumari","year":"2021","unstructured":"Kumari, R., Ekbal, A.: AMFB: attention based multimodal factorized bilinear pooling for multimodal fake news detection. Expert Syst. Appl. 184, 115412 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115412","journal-title":"Expert Syst. Appl."},{"key":"1715_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119302","volume":"215","author":"P Singh","year":"2023","unstructured":"Singh, P., Srivastava, R., Rana, K., Kumar, V.: Semi-FND: stacked ensemble based multimodal inferencing framework for faster fake news detection. Expert Syst. Appl. 215, 119302 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.119302","journal-title":"Expert Syst. Appl."},{"key":"1715_CR35","doi-asserted-by":"publisher","unstructured":"Qian, S., Wang, J., Hu, J., Fang, Q., Xu, C.: Hierarchical multi-modal contextual attention network for fake news detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, NY, USA, SIGIR \u201921, pp. 153\u2013162 (2021) https:\/\/doi.org\/10.1145\/3404835.3462871","DOI":"10.1145\/3404835.3462871"},{"key":"1715_CR36","doi-asserted-by":"publisher","unstructured":"Wu, Y., Zhan, P., Zhang, Y., Wang, L., Xu, Z.: Multimodal fusion with co-attention networks for fake news detection. In: Zong C, Xia F, Li W, Navigli R (eds) Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Association for Computational Linguistics, Online, pp. 2560\u20132569 (2021) https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.226","DOI":"10.18653\/v1\/2021.findings-acl.226"},{"key":"1715_CR37","doi-asserted-by":"publisher","unstructured":"Bai, Y., Cao, M., Gao, D., Cao, Z., Chen, C., Fan, Z., Nie, L., Zhang, M.: Rasa: relation and sensitivity aware representation learning for text-based person search. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI \u201923 (2023) https:\/\/doi.org\/10.24963\/ijcai.2023\/62,","DOI":"10.24963\/ijcai.2023\/62"},{"issue":"11","key":"1715_CR38","doi-asserted-by":"publisher","first-page":"11141","DOI":"10.1109\/TKDE.2022.3231338","volume":"35","author":"L Hu","year":"2023","unstructured":"Hu, L., Chen, Z., Zhao, Z., Yin, J., Nie, L.: Causal inference for leveraging image-text matching bias in multi-modal fake news detection. IEEE Trans. Knowl. Data Eng. 35(11), 11141\u201311152 (2023). https:\/\/doi.org\/10.1109\/TKDE.2022.3231338","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01715-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01715-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01715-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T19:34:58Z","timestamp":1745264098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01715-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,18]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1715"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01715-7","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,18]]},"assertion":[{"value":"16 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"119"}}