{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T08:57:25Z","timestamp":1770800245366,"version":"3.50.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"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":[[2026,2]]},"DOI":"10.1007\/s00530-025-02107-7","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T06:04:31Z","timestamp":1765605871000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCLR: mitigating modality imbalance in multimodal fake news detection via dynamic contrastive learning with re-initialization"],"prefix":"10.1007","volume":"32","author":[{"given":"Zhenyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"issue":"8","key":"2107_CR1","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed, M., Seraj, R., Islam, S.M.S.: The k-means algorithm: A comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020). https:\/\/doi.org\/10.3390\/electronics9081295","journal-title":"Electronics"},{"key":"2107_CR2","unstructured":"Alabdulmohsin, I., Maennel, H., Keysers, D.: The impact of reinitialization on generalization in convolutional neural networks. (2021) arXiv: https:\/\/arxiv.org\/abs\/2109.00267"},{"key":"2107_CR3","doi-asserted-by":"publisher","unstructured":"Alam, F., Cresci, S., Chakraborty, T., et\u00a0al.: A survey on multimodal disinformation detection. In: Proceedings of the 29th International Conference on Computational Linguistics, pp 6625\u20136643,(2022) https:\/\/doi.org\/10.48550\/arXiv.2103.12541","DOI":"10.48550\/arXiv.2103.12541"},{"issue":"2","key":"2107_CR4","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1257\/jep.31.2.211","volume":"31","author":"H Allcott","year":"2017","unstructured":"Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Journal of Economic Perspectives 31(2), 211\u2013236 (2017). https:\/\/doi.org\/10.1257\/jep.31.2.211","journal-title":"Journal of Economic Perspectives"},{"key":"2107_CR5","doi-asserted-by":"publisher","unstructured":"Arpit, D., Jastrz\u0119bski, S., Ballas, N., et\u00a0al.: A closer look at memorization in deep networks. In: Proceedings of the 34th International Conference on Machine Learning, pp 233\u2013242, (2017)https:\/\/doi.org\/10.48550\/arXiv.1706.05394","DOI":"10.48550\/arXiv.1706.05394"},{"key":"2107_CR6","doi-asserted-by":"publisher","first-page":"3884","DOI":"10.48550\/arXiv.1910.08475","volume":"33","author":"J Ash","year":"2020","unstructured":"Ash, J., Adams, R.P.: On warm-starting neural network training. Advances in Neural Information Processing Systems 33, 3884\u20133894 (2020). https:\/\/doi.org\/10.48550\/arXiv.1910.08475","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"2107_CR7","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s13735-017-0143-x","volume":"7","author":"C Boididou","year":"2018","unstructured":"Boididou, C., Papadopoulos, S., Zampoglou, M., et al.: Detection and visualization of misleading content on twitter. International Journal of Multimedia Information Retrieval 7(1), 71\u201386 (2018). https:\/\/doi.org\/10.1007\/s13735-017-0143-x","journal-title":"International Journal of Multimedia Information Retrieval"},{"key":"2107_CR8","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1145\/3485447.3511968","volume":"2022","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Li, D., Zhang, P., et al.: Cross-modal ambiguity learning for multimodal fake news detection. Proceedings of the ACM Web Conference 2022, 2897\u20132905 (2022). https:\/\/doi.org\/10.1145\/3485447.3511968","journal-title":"Proceedings of the ACM Web Conference"},{"issue":"2","key":"2107_CR9","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s00530-025-01715-7","volume":"31","author":"S Cui","year":"2025","unstructured":"Cui, S., Duan, K., Ma, W., et al.: Ccgn: Consistency contrastive-learning graph network for multi-modal fake news detection. Multimedia Syst. 31(2), 119 (2025). https:\/\/doi.org\/10.1007\/s00530-025-01715-7","journal-title":"Multimedia Syst."},{"key":"2107_CR10","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, MW., Lee, K., et\u00a0al.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4171\u20134186, (2019) https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"2107_CR11","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. (2020) arXiv: https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"2107_CR12","unstructured":"Du, C., Li, T., Liu, Y., et\u00a0al.: Improving multi-modal learning with uni-modal teachers. (2021) arXiv:https:\/\/arxiv.org\/abs\/2106.11059"},{"key":"2107_CR13","doi-asserted-by":"publisher","unstructured":"Fan, Y., Xu, W., Wang, H., et\u00a0al.: Pmr: Prototypical modal rebalance for multimodal learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 20029\u201320038, (2023) https:\/\/doi.org\/10.1109\/cvpr52729.2023.01918","DOI":"10.1109\/cvpr52729.2023.01918"},{"key":"2107_CR14","doi-asserted-by":"publisher","unstructured":"Goyal, Y., Khot, T., Summers-Stay, D., et\u00a0al.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6904\u20136913,(2017) https:\/\/doi.org\/10.1109\/CVPR.2017.670","DOI":"10.1109\/CVPR.2017.670"},{"key":"2107_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110125","volume":"136","author":"J Hua","year":"2023","unstructured":"Hua, J., Cui, X., Li, X., et al.: Multimodal fake news detection through data augmentation-based contrastive learning. Appl. Soft Comput. 136, 110125 (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110125","journal-title":"Appl. Soft Comput."},{"key":"2107_CR16","doi-asserted-by":"publisher","unstructured":"Jin, Z., Cao, J., Guo, H., et\u00a0al.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM International Conference on Multimedia, pp 795\u2013816, (2017a) https:\/\/doi.org\/10.1145\/3123266.3123454","DOI":"10.1145\/3123266.3123454"},{"key":"2107_CR17","doi-asserted-by":"publisher","unstructured":"Jin, Z., Cao, J., Guo, H., et\u00a0al.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM International Conference on Multimedia, pp 795\u2013816, (2017b) https:\/\/doi.org\/10.1145\/3123266.3123454","DOI":"10.1145\/3123266.3123454"},{"key":"2107_CR18","doi-asserted-by":"publisher","first-page":"2915","DOI":"10.1145\/3308558.3313552","volume":"2019","author":"D Khattar","year":"2019","unstructured":"Khattar, D., Goud, J.S., Gupta, M., et al.: Mvae: Multimodal variational autoencoder for fake news detection. Proceedings of the World Wide Web Conference 2019, 2915\u20132921 (2019). https:\/\/doi.org\/10.1145\/3308558.3313552","journal-title":"Proceedings of the World Wide Web Conference"},{"key":"2107_CR19","doi-asserted-by":"publisher","first-page":"18661","DOI":"10.48550\/arXiv.2004.11362","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., Teterwak, P., Wang, C., et al.: Supervised contrastive learning. Advances in Neural Information Processing Systems 33, 18661\u201318673 (2020). https:\/\/doi.org\/10.48550\/arXiv.2004.11362","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2107_CR20","unstructured":"Liu, Y., Ott, M., Goyal, N., et\u00a0al.: Roberta: A robustly optimized bert pretraining approach. (2019)t arXiv: https:\/\/arxiv.org\/abs\/1907.11692"},{"key":"2107_CR21","doi-asserted-by":"publisher","unstructured":"Lu, YJ., Li, CT.: Gcan: Graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 505\u2013514, (2020) https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.48","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"2107_CR22","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wei, Z., et\u00a0al.: Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 1751\u20131754, (2015) https:\/\/doi.org\/10.1145\/2806416.2806607","DOI":"10.1145\/2806416.2806607"},{"key":"2107_CR23","unstructured":"MA, J., GAO, W., MITRA, P., et\u00a0al.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp 3818\u20133824(2016)"},{"key":"2107_CR24","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wong, KF.: Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp 708\u2013717, (2017) https:\/\/doi.org\/10.18653\/v1\/P17-1066","DOI":"10.18653\/v1\/P17-1066"},{"issue":"11","key":"2107_CR25","first-page":"2579","volume":"9","author":"M Lvd","year":"2008","unstructured":"Lvd, M., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"2107_CR26","unstructured":"MacQueen, J., et\u00a0al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 15th Berkeley Symposium on Mathematical Statistics and Probability, pp 281\u2013297(1967)"},{"issue":"1","key":"2107_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103564","volume":"61","author":"L Peng","year":"2024","unstructured":"Peng, L., Jian, S., Kan, Z., et al.: Not all fake news is semantically similar: Contextual semantic representation learning for multimodal fake news detection. Information Processing & Management 61(1), 103564 (2024). https:\/\/doi.org\/10.1016\/j.ipm.2023.103564","journal-title":"Information Processing & Management"},{"key":"2107_CR28","doi-asserted-by":"publisher","unstructured":"Peng, X., Wei, Y., Deng, A., et\u00a0al.: Balanced multimodal learning via on-the-fly gradient modulation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8238\u20138247, (2022) https:\/\/doi.org\/10.1109\/CVPR52688.2022.00806","DOI":"10.1109\/CVPR52688.2022.00806"},{"key":"2107_CR29","doi-asserted-by":"publisher","unstructured":"Qian, S., Wang, J., Hu, J., et\u00a0al.: 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, pp 153\u2013162, (2021) https:\/\/doi.org\/10.1145\/3404835.3462871","DOI":"10.1145\/3404835.3462871"},{"key":"2107_CR30","doi-asserted-by":"publisher","unstructured":"Radford, A., Kim, JW., Hallacy, C., et\u00a0al.: Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, pp 8748\u20138763, (2021) https:\/\/doi.org\/10.48550\/arXiv.2103.00020","DOI":"10.48550\/arXiv.2103.00020"},{"issue":"1","key":"2107_CR31","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu, K., Sliva, A., Wang, S., et al.: Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsl 19(1), 22\u201336 (2017). https:\/\/doi.org\/10.1145\/3137597.3137600","journal-title":"ACM SIGKDD Explorations Newsl"},{"key":"2107_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition.(2014) arXiv:1409.1556"},{"key":"2107_CR33","doi-asserted-by":"publisher","unstructured":"Singhal, S., Shah, RR., Chakraborty, T., et\u00a0al.: Spotfake: A multi-modal framework for fake news detection. In: Proceedings of the 2019 IEEE 15th International Conference on Multimedia Big Data, pp 39\u201347, (2019) https:\/\/doi.org\/10.1109\/BigMM.2019.00-44","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"2107_CR34","doi-asserted-by":"publisher","unstructured":"Singhal, S., Kabra, A., Sharma, M., et\u00a0al.: Spotfake+: A multimodal framework for fake news detection via transfer learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 13915\u201313916, (2020) https:\/\/doi.org\/10.1609\/aaai.v34i10.7230","DOI":"10.1609\/aaai.v34i10.7230"},{"key":"2107_CR35","doi-asserted-by":"publisher","unstructured":"Sokar, G., Agarwal, R., Castro, PS., et\u00a0al.: The dormant neuron phenomenon in deep reinforcement learning. In: Proceedings of the 40th International Conference on Machine Learning, pp 32145\u201332168, (2023) https:\/\/doi.org\/10.48550\/arXiv.2302.12902","DOI":"10.48550\/arXiv.2302.12902"},{"key":"2107_CR36","doi-asserted-by":"publisher","unstructured":"Sun, T., Qian, Z., Li, P., et\u00a0al.: Graph interactive network with adaptive gradient for multi-modal rumor detection. In: Proceedings of the 2023 ACM international conference on multimedia retrieval, pp 316\u2013324, (2023) https:\/\/doi.org\/10.1145\/3591106.3592250","DOI":"10.1145\/3591106.3592250"},{"key":"2107_CR37","doi-asserted-by":"publisher","unstructured":"Taha, A., Shrivastava, A., Davis, LS.: Knowledge evolution in neural networks. In: Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 12843\u201312852, (2021) https:\/\/doi.org\/10.1109\/CVPR46437.2021.01265","DOI":"10.1109\/CVPR46437.2021.01265"},{"key":"2107_CR38","doi-asserted-by":"publisher","unstructured":"Volkova, S., Shaffer, K., Jang, JY., et\u00a0al.: Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp 647\u2013653, (2017) https:\/\/doi.org\/10.18653\/v1\/P17-2102","DOI":"10.18653\/v1\/P17-2102"},{"key":"2107_CR39","doi-asserted-by":"publisher","unstructured":"Wang, W., Tran, D., Feiszli, M.: What makes training multi-modal classification networks hard? In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12695\u201312705, (2020) https:\/\/doi.org\/10.1109\/CVPR42600.2020.01271","DOI":"10.1109\/CVPR42600.2020.01271"},{"key":"2107_CR40","doi-asserted-by":"publisher","unstructured":"Wang, Y., Ma, F., Jin, Z., et\u00a0al.: Eann: Event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 849\u2013857, (2018) https:\/\/doi.org\/10.1145\/3219819.3219903","DOI":"10.1145\/3219819.3219903"},{"key":"2107_CR41","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2025.3559114","author":"F Wu","year":"2025","unstructured":"Wu, F., Chen, S., Deng, Z.Y., et al.: Pfbl: Prototype-based fully balanced learning for multimodal fake news detection. IEEE Transactions on Computational Social Systems (2025). https:\/\/doi.org\/10.1109\/TCSS.2025.3559114","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"2107_CR42","doi-asserted-by":"publisher","first-page":"2560","DOI":"10.18653\/v1\/2021.findings-acl.226","volume":"2021","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Zhan, P., Zhang, Y., et al.: Multimodal fusion with co-attention networks for fake news detection. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2560\u20132569 (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.226","journal-title":"Findings of the Association for Computational Linguistics: ACL-IJCNLP"},{"key":"2107_CR43","unstructured":"Xia, X., Liu, T., Han, B., et\u00a0al.: Robust early-learning: Hindering the memorization of noisy labels. In: Proceedings of 2021 International Conference on Learning Representations, pp 1\u201315 (2021)"},{"key":"2107_CR44","doi-asserted-by":"publisher","unstructured":"Xu, R., Feng, R., Zhang, SX., et\u00a0al.: Mmcosine: Multi-modal cosine loss towards balanced audio-visual fine-grained learning. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1\u20135, (2023) https:\/\/doi.org\/10.48550\/arXiv.2303.05338","DOI":"10.48550\/arXiv.2303.05338"},{"key":"2107_CR45","doi-asserted-by":"publisher","unstructured":"Yang, Z., Dai, Z., Yang, Y., et\u00a0al.: Xlnet: Generalized autoregressive pretraining for language understanding. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp 5753\u20135763, (2019) https:\/\/doi.org\/10.48550\/arXiv.1906.08237","DOI":"10.48550\/arXiv.1906.08237"},{"key":"2107_CR46","doi-asserted-by":"publisher","unstructured":"Zaidi, S., Berariu, T., Kim, H., et\u00a0al.: When does re-initialization work? In: Proceedings on \"I Can\u2019t Believe It\u2019s Not Better! - Understanding Deep Learning Through Empirical Falsification\" at NeurIPS 2022 Workshops, pp 12\u201326, (2023) https:\/\/doi.org\/10.48550\/arXiv.2206.10011","DOI":"10.48550\/arXiv.2206.10011"},{"issue":"10","key":"2107_CR47","doi-asserted-by":"publisher","first-page":"14112","DOI":"10.1109\/TNNLS.2023.3274694","volume":"35","author":"Q Zhang","year":"2024","unstructured":"Zhang, Q., Yang, Y., Shi, C., et al.: Rumor detection with hierarchical representation on bipartite ad hoc event trees. IEEE Transactions on Neural Networks and Learning Systems 35(10), 14112\u201314124 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2023.3274694","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2107_CR48","doi-asserted-by":"publisher","unstructured":"Zhou, X., Wu, J., Zafarani, R.: Safe: Similarity-aware multi-modal fake news detection. In: Proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 354\u2013367, (2020)https:\/\/doi.org\/10.1007\/978-3-030-47436-2_27","DOI":"10.1007\/978-3-030-47436-2_27"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02107-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-02107-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02107-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T04:19:57Z","timestamp":1770783597000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-02107-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2107"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-02107-7","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"5 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"45"}}