{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T15:31:23Z","timestamp":1772033483985,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819541577","type":"print"},{"value":"9789819541584","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-4158-4_1","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:41:53Z","timestamp":1767325313000},"page":"3-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Collaborative Stance Detection via\u00a0Small-Large Language Model Consistency Verification"],"prefix":"10.1007","author":[{"given":"Yu","family":"Yan","sequence":"first","affiliation":[]},{"given":"Sheng","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zixiang","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Teli","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Allaway, E., Mckeown, K.: Zero-shot stance detection: A dataset and model using generalized topic representations. In: EMNLP 2020, pp. 8913\u20138931 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.717"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Allaway, E., Srikanth, M., McKeown, K.: Adversarial learning for zero-shot stance detection on social media. arXiv preprint arXiv:2105.06603 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.379"},{"key":"1_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1007\/978-3-540-76298-0_52","volume-title":"The Semantic Web","author":"S Auer","year":"2007","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC\/ISWC -2007. LNCS, vol. 4825, pp. 722\u2013735. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-76298-0_52"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Xiao, S., Zhang, P., et al.: M3-embedding: multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. In: Findings of ACL 2024, pp. 2318\u20132335 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"1_CR5","unstructured":"Cruickshank, I.J., Xian\u00a0Ng, L.H.: Use of large language models for stance classification. arXiv e-prints pp. arXiv\u20132309 (2023)"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Lan, X., Gao, C., Jin, D., Li, Y.: Stance detection with collaborative role-infused LLM-based agents. In: AAAI 2024, vol.\u00a018, pp. 891\u2013903 (2024)","DOI":"10.1609\/icwsm.v18i1.31360"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Li, A., Liang, B., Zhao, J., Zhang, B., Yang, M., Xu, R.: Stance detection on social media with background knowledge. In: EMNLP 2023, pp. 15703\u201315717 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.972"},{"key":"1_CR8","unstructured":"Li, A., et al.: Mitigating biases of large language models in stance detection with calibration. arXiv preprint arXiv:2402.14296 (2024)"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Sosea, T., Sawant, A., Nair, A.J., Inkpen, D., Caragea, C.: P-stance: a large dataset for stance detection in political domain. In: ACL-IJCNLP Findings, pp. 2355\u20132365 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.208"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Liang, B.e.a.: JointCL: joint contrastive learning framework for zero-shot stance detection. In: ACL, pp. 81\u201391 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.470"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Liang, B., et al.: Zero-shot stance detection via contrastive learning. In: WWW, pp. 2738\u20132747 (2022)","DOI":"10.1145\/3485447.3511994"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Liu, R., Lin, Z., Tan, Y., Wang, W.: Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. In: ACL-IJCNLP 2021. pp. 3152\u20133157 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.278"},{"key":"1_CR13","unstructured":"Liu, Y., et al.: RoBERTa. arXiv preprint (2019). arXiv:1907.11692"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Mohammad, S., et al.: SemEval-2016 task 6: detecting stance in tweets. In: SemEval, pp. 31\u201341 (2016)","DOI":"10.18653\/v1\/S16-1003"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Sobhani, P., et al.: Dataset for multi-target stance detection. In: EACL, pp. 551\u2013557 (2017)","DOI":"10.18653\/v1\/E17-2088"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: AAAI, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"1_CR17","unstructured":"Wang, J., Wang, C., Tan, C., Huang, J., Gao, M.: Boosting in-context learning with factual knowledge. arXiv preprint arXiv:2309.14771 (2023)"},{"key":"1_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1007\/978-3-319-50496-4_85","volume-title":"Natural Language Understanding and Intelligent Applications","author":"R Xu","year":"2016","unstructured":"Xu, R., Zhou, Yu., Wu, D., Gui, L., Du, J., Xue, Y.: Overview of NLPCC shared task 4: stance detection in Chinese microblogs. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL\/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 907\u2013916. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50496-4_85"},{"key":"1_CR19","unstructured":"Zhang, B., Ding, D., Jing, L.: Future of stance detection techniques post ChatGPT. arXiv preprint (2022), arXiv:2212.14548"},{"key":"1_CR20","unstructured":"Zhang, B., Ding, D., Jing, L., Huang, H.: A logically consistent chain-of-thought approach for stance detection. arXiv preprint arXiv:2312.16054 (2023)"},{"key":"1_CR21","unstructured":"Zhang, B., Fu, X., Ding, D., Huang, H., Li, Y., Jing, L.: Investigating chain-of-thought with chatgpt for stance detection on social media. arXiv preprint arXiv:2304.03087 (2023)"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Liang, B., Sun, J., Du, J., Zhou, L., Xu, R.: Enhancing zero-shot stance detection via targeted background knowledge. In: ACM SIGIR. pp. 2070\u20132075 (2022)","DOI":"10.1145\/3477495.3531807"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4158-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:41:56Z","timestamp":1767325316000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4158-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819541577","9789819541584"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4158-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}