{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:40:49Z","timestamp":1774078849696,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62466029"],"award-info":[{"award-number":["62466029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s13042-026-03041-4","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:53:47Z","timestamp":1774068827000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero-shot stance detection incorporating sentiment knowledge in a joint contrastive learning framework"],"prefix":"10.1007","volume":"17","author":[{"given":"Hongbin","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jiasheng","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"3041_CR1","doi-asserted-by":"crossref","unstructured":"Du J, Xu R, He Y, et al. (2017) Stance classification with target-specific neural attention networks. In: 26th International joint conference on artificial intelligence, IJCAI 2017, international joint conferences on artificial intelligence, pp 3988\u20133994","DOI":"10.24963\/ijcai.2017\/557"},{"key":"3041_CR2","unstructured":"Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. Advances in neural information processing systems, 28"},{"key":"3041_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102386","volume":"108","author":"X Zhao","year":"2024","unstructured":"Zhao X, Zou J, Miao J et al (2024) Zero-shot stance detection based on multi-perspective transferable feature fusion. Inf Fusion 108:102386","journal-title":"Inf Fusion"},{"key":"3041_CR4","doi-asserted-by":"crossref","unstructured":"Allaway E, McKeown K (2020) Zero-shot stance detection: a dataset and model using generalized topic representations. arXiv preprint arXiv:2010.03640","DOI":"10.18653\/v1\/2020.emnlp-main.717"},{"key":"3041_CR5","unstructured":"Nkhata G, Gauch S (2025) Intermediate-task transfer learning: leveraging sarcasm detection for stance detection. arXiv preprint arXiv:2503.03172"},{"key":"3041_CR6","doi-asserted-by":"crossref","unstructured":"Ding D, Dong L, Huang Z, et al. (2024) Edda: a encoder-decoder data augmentation framework for zero-shot stance detection. arXiv preprint arXiv:2403.15715","DOI":"10.63317\/5fvfixthhpye"},{"key":"3041_CR7","doi-asserted-by":"crossref","unstructured":"Zhang Z, Li Y, Zhang J, et al. (2024) Llm-driven knowledge injection advances zero-shot and cross-target stance detection. In: Proceedings of the 2024 conference of the North American Chapter of the Association for computational linguistics: human language technologies (volume 2: short papers), pp 371\u2013378","DOI":"10.18653\/v1\/2024.naacl-short.32"},{"key":"3041_CR8","unstructured":"Luo Y, Liu Z, Shi Y, et al. (2022) Exploiting sentiment and common sense for zero-shot stance detection. arXiv preprint arXiv:2208.08797"},{"key":"3041_CR9","doi-asserted-by":"crossref","unstructured":"Santosh T, Bansal S, Saha A (2019) Can siamese networks help in stance detection? In: Proceedings of the ACM India joint international conference on data science and management of data, pp 306\u2013309","DOI":"10.1145\/3297001.3297047"},{"key":"3041_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128849","volume":"614","author":"Y Ding","year":"2025","unstructured":"Ding Y, Lei Y, Wang A et al (2025) Adversarial contrastive representation training with external knowledge injection for zero-shot stance detection. Neurocomputing 614:128849","journal-title":"Neurocomputing"},{"issue":"2","key":"3041_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40747-024-01767-8","volume":"11","author":"Q Fan","year":"2025","unstructured":"Fan Q, Lu J, Sun Y et al (2025) Enhancing zero-shot stance detection via multi-task fine-tuning with debate data and knowledge augmentation. Complex Intell Syst 11(2):1\u201312","journal-title":"Complex Intell Syst"},{"key":"3041_CR12","doi-asserted-by":"crossref","unstructured":"Zhao C, Caragea C (2024) Ez-stance: a large dataset for english zero-shot stance detection. In: Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: long papers), pp 15697\u201315714","DOI":"10.18653\/v1\/2024.acl-long.838"},{"key":"3041_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126943","volume":"563","author":"H Zhang","year":"2024","unstructured":"Zhang H, Li Y, Zhu T et al (2024) Commonsense-based adversarial learning framework for zero-shot stance detection. Neurocomputing 563:126943","journal-title":"Neurocomputing"},{"key":"3041_CR14","unstructured":"Ju W, Wang Y, Qin Y, et al. (2024) Towards graph contrastive learning: a survey and beyond. arXiv preprint arXiv:2405.11868"},{"key":"3041_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123956","volume":"250","author":"C Wang","year":"2024","unstructured":"Wang C, Zhang Y, Wang S (2024) A meta-contrastive learning with data augmentation framework for zero-shot stance detection. Expert Syst Appl 250:123956","journal-title":"Expert Syst Appl"},{"key":"3041_CR16","first-page":"2738","volume":"2022","author":"B Liang","year":"2022","unstructured":"Liang B, Chen Z, Gui L et al (2022) Zero-shot stance detection via contrastive learning. Proc ACM Web Conf 2022:2738\u20132747","journal-title":"Proc ACM Web Conf"},{"key":"3041_CR17","doi-asserted-by":"crossref","unstructured":"Tian Y, Krishnan D, Isola P (2020) Contrastive multiview coding. In: Computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI 16, Springer, pp 776\u2013794","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"3041_CR18","unstructured":"Gunel B, Du J, Conneau A, et al. (2020) Supervised contrastive learning for pre-trained language model fine-tuning. arXiv preprint arXiv:2011.01403"},{"key":"3041_CR19","doi-asserted-by":"crossref","unstructured":"Kachuee M, Yuan H, Kim YB, et al. (2020) Self-supervised contrastive learning for efficient user satisfaction prediction in conversational agents. arXiv preprint arXiv:2010.11230","DOI":"10.18653\/v1\/2021.naacl-main.319"},{"key":"3041_CR20","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang X, Han X, et al. (2021) Cleve: contrastive pre-training for event extraction. arXiv preprint arXiv:2105.14485","DOI":"10.18653\/v1\/2021.acl-long.491"},{"key":"3041_CR21","unstructured":"Li J, Zhou P, Xiong C, et al. (2020) Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966"},{"key":"3041_CR22","doi-asserted-by":"crossref","unstructured":"Augenstein I, Rockt\u00e4schel T, Vlachos A, et al. (2016) Stance detection with bidirectional conditional encoding. arXiv preprint arXiv:1606.05464","DOI":"10.18653\/v1\/D16-1084"},{"key":"3041_CR23","doi-asserted-by":"crossref","unstructured":"Xu C, Paris C, Nepal S, et al. (2018) Cross-target stance classification with self-attention networks. arXiv preprint arXiv:1805.06593","DOI":"10.18653\/v1\/P18-2123"},{"key":"3041_CR24","doi-asserted-by":"crossref","unstructured":"Zhang B, Yang M, Li X, et al. (2020) Enhancing cross-target stance detection with transferable semantic-emotion knowledge. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3188\u20133197","DOI":"10.18653\/v1\/2020.acl-main.291"},{"key":"3041_CR25","first-page":"3453","volume":"2021","author":"B Liang","year":"2021","unstructured":"Liang B, Fu Y, Gui L et al (2021) Target-adaptive graph for cross-target stance detection. Proc Web Conf 2021:3453\u20133464","journal-title":"Proc Web Conf"},{"key":"3041_CR26","doi-asserted-by":"crossref","unstructured":"Allaway E, Srikanth M, McKeown K (2021) Adversarial learning for zero-shot stance detection on social media. arXiv preprint arXiv:2105.06603","DOI":"10.18653\/v1\/2021.naacl-main.379"},{"key":"3041_CR27","unstructured":"Devlin J, Chang MW, Lee K, et al (2019) 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, volume 1 (long and short papers), pp 4171\u20134186"},{"key":"3041_CR28","doi-asserted-by":"crossref","unstructured":"Liu R, Lin Z, Tan Y et al (2021) Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. Findings of the association for computational linguistics ACL-IJCNLP 2021:3152\u20133157","DOI":"10.18653\/v1\/2021.findings-acl.278"},{"key":"3041_CR29","doi-asserted-by":"crossref","unstructured":"Liang B, Zhu Q, Li X, et al. (2022) Jointcl: A joint contrastive learning framework for zero-shot stance detection. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), Association for Computational Linguistics, pp 81\u201391","DOI":"10.18653\/v1\/2022.acl-long.7"},{"key":"3041_CR30","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H (2020) Exploiting cloze questions for few shot text classification and natural language inference. arXiv preprint arXiv:2001.07676","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"3041_CR31","doi-asserted-by":"crossref","unstructured":"Li Y, Yuan J (2022) Generative data augmentation with contrastive learning for zero-shot stance detection. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 6985\u20136995","DOI":"10.18653\/v1\/2022.emnlp-main.470"},{"key":"3041_CR32","doi-asserted-by":"crossref","unstructured":"Liu R, Lin Z, Fu P et al (2022) Connecting targets via latent topics and contrastive learning: a unified framework for robust zero-shot and few-shot stance detection. ICASSP 2022\u20132022 IEEE International Conference on Acoustics. IEEE, Speech and Signal Processing (ICASSP), pp 7812\u20137816","DOI":"10.1109\/ICASSP43922.2022.9746739"},{"issue":"1","key":"3041_CR33","doi-asserted-by":"publisher","first-page":"18092","DOI":"10.1038\/s41598-024-68870-1","volume":"14","author":"X Zhao","year":"2024","unstructured":"Zhao X, Ma G, Pang S et al (2024) Zero-shot stance detection based on multi-expert collaboration. Sci Rep 14(1):18092","journal-title":"Sci Rep"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03041-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03041-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03041-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:53:52Z","timestamp":1774068832000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03041-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,21]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["3041"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03041-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,21]]},"assertion":[{"value":"8 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"199"}}