{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T19:30:11Z","timestamp":1786995011064,"version":"build-2736575974"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Stance detection aims to identify an author\u2019s stance toward a target topic. Although recent prompting-based methods enable zero- and few-shot stance detection with large language models (LLMs), they often require extensive prompting or costly use of multiple LLM agents, as well as large labeled training datasets. We present MINE (Meta-prompting and In-context N-shot Examples), a simple, light-weight, training-free framework that first creates a target-specific LLM stance agent through meta-prompting, and then augments it with a small number of semantically retrieved examples. While meta-prompting and retrieval-augmented in-context learning have been studied independently, these components have not been combined specifically for stance detection in a training-free pipeline to the best of our knowledge. We also provide a systematic empirical study that quantifies the effects of the example retrieval strategy, sentence encoder choice, demonstrator pool size, and the number of examples on the performance of this hybrid methodology. We show that MINE achieves competitive performance when deployed as either a zero-shot or few-shot stance detector, matching or exceeding existing prompting-only approaches on SemEval-2016 and P-Stance while using fewer prompts and demonstrations. MINE remains effective with minimal labeled data (e.g., 10% of the training demonstrator pool) and generalizes to related tasks, including aspect-based sentiment analysis and cross-target stance detection.<\/jats:p>","DOI":"10.1007\/s41060-026-01245-z","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T18:39:35Z","timestamp":1786991975000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving few-shot stance detection with MINE: meta-prompting and in-context N-shot examples"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3937-7299","authenticated-orcid":false,"given":"Uwaila","family":"Ekhator","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-9728","authenticated-orcid":false,"given":"Francesca","family":"Spezzano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"1245_CR1","doi-asserted-by":"publisher","unstructured":"Upadhyaya, A., Fisichella, M., Nejdl, W.: Intensity-valued emotions help stance detection of climate change twitter data. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. IJCAI \u201923, (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/693","DOI":"10.24963\/ijcai.2023\/693"},{"key":"1245_CR2","doi-asserted-by":"publisher","unstructured":"Ma, J., Wang, C., Xing, H., Zhao, D., Zhang, Y.: Chain of stance: Stance detection with large language models. In: Natural Language Processing and Chinese Computing: 13th National CCF Conference, NLPCC 2024, Hangzhou, China, November 1\u20133, 2024, Proceedings, Part V, pp. 82\u201394. Springer, Berlin, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-981-97-9443-0_7","DOI":"10.1007\/978-981-97-9443-0_7"},{"key":"1245_CR3","doi-asserted-by":"crossref","unstructured":"Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. 17(3), (2017). https:\/\/doi.org\/10.1145\/3003433","DOI":"10.1145\/3003433"},{"issue":"6","key":"1245_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103070","volume":"59","author":"LHX Ng","year":"2022","unstructured":"Ng, L.H.X., Carley, K.M.: Is my stance the same as your stance? a cross validation study of stance detection datasets. Information Processing & Management 59(6), 103070 (2022). https:\/\/doi.org\/10.1016\/j.ipm.2022.103070","journal-title":"Information Processing & Management"},{"key":"1245_CR5","doi-asserted-by":"crossref","unstructured":"Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.-Y., Wen, J.-R.: A Survey of Large Language Models (2025). arXiv: 2303.18223","DOI":"10.1007\/s11704-026-60308-3"},{"key":"1245_CR6","doi-asserted-by":"publisher","unstructured":"Lan, X., Gao, C., Jin, D., Li, Y.: Stance detection with collaborative role-infused llm-based agents. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 18, pp. 891\u2013903. (2024). https:\/\/doi.org\/10.1609\/icwsm.v18i1.31360","DOI":"10.1609\/icwsm.v18i1.31360"},{"key":"1245_CR7","doi-asserted-by":"publisher","unstructured":"Li, A., Liang, B., Zhao, J., Zhang, B., Yang, M., Xu, R.: Stance detection on social media with background knowledge. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15703\u201315717. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.972 . https:\/\/aclanthology.org\/2023.emnlp-main.972\/","DOI":"10.18653\/v1\/2023.emnlp-main.972"},{"key":"1245_CR8","doi-asserted-by":"publisher","unstructured":"Huang, H., Zhang, B., Li, Y., Zhang, B., Sun, Y., Luo, C., Peng, C.: Knowledge-enhanced prompt-tuning for stance detection. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 22(6) (2023) https:\/\/doi.org\/10.1145\/3588767","DOI":"10.1145\/3588767"},{"key":"1245_CR9","doi-asserted-by":"crossref","unstructured":"Chen, D., Song, S., Yu, Q., Li, Z., Wang, W., Xiong, F., Tang, B.: Grimoire is all you need for enhancing large language models, (2024). arXiv:2401.03385 arXiv preprint","DOI":"10.21203\/rs.3.rs-3845612\/v1"},{"key":"1245_CR10","doi-asserted-by":"publisher","unstructured":"Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., Chen, W.: What makes good in-context examples for GPT-3? In: Agirre, E., Apidianaki, M., Vuli\u0107, I. (eds.) Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pp. 100\u2013114. Association for Computational Linguistics, Dublin, Ireland and Online (2022). https:\/\/doi.org\/10.18653\/v1\/2022.deelio-1.10 . https:\/\/aclanthology.org\/2022.deelio-1.10\/","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"1245_CR11","doi-asserted-by":"publisher","unstructured":"Zou, M., Guo, R., Zhang, S., Zhang, X., Feng, Z.: Tat: Improving stance detection on social media through thought alignment with llms. In: Companion Proceedings of the ACM on Web Conference 2025. WWW \u201925, pp. 1558\u20131562. Association for Computing Machinery, New York, NY, USA (2025). https:\/\/doi.org\/10.1145\/3701716.3715465","DOI":"10.1145\/3701716.3715465"},{"key":"1245_CR12","doi-asserted-by":"crossref","unstructured":"K\u00fc\u00e7\u00fck, D., Can, F.: Stance detection: A survey. ACM Comput. Surv. 53(1), (2020). https:\/\/doi.org\/10.1145\/3369026","DOI":"10.1145\/3369026"},{"key":"1245_CR13","doi-asserted-by":"publisher","unstructured":"Gera, P., Neal, T.: Deep learning in stance detection: A survey. ACM Comput. Surv. 58(1) (2025) https:\/\/doi.org\/10.1145\/3744641","DOI":"10.1145\/3744641"},{"key":"1245_CR14","unstructured":"Kabaghe, C., Qin, J.: Classifying tweets based on climate change stance natural language processing. (2019). https:\/\/api.semanticscholar.org\/CorpusID:203642878"},{"key":"1245_CR15","doi-asserted-by":"publisher","unstructured":"Wang, Z., Sun, Q., Li, S., Zhu, Q., Zhou, G.: Neural stance detection with hierarchical linguistic representations. IEEE\/ACM Trans. Audio, Speech and Lang. Proc 28, 635\u2013645 (2020). https:\/\/doi.org\/10.1109\/TASLP.2020.2963954","DOI":"10.1109\/TASLP.2020.2963954"},{"key":"1245_CR16","doi-asserted-by":"crossref","unstructured":"Chen, X., Zou, L., Zhao, B.: Detecting climate change deniers on twitter using a deep neural network. In: International Conference on Machine Learning and Computing (2019). https:\/\/api.semanticscholar.org\/CorpusID:160010269","DOI":"10.1145\/3318299.3318382"},{"key":"1245_CR17","doi-asserted-by":"publisher","unstructured":"Vychegzhanin, S., Kotelnikov, E.: A new method for stance detection based on feature selection techniques and ensembles of classifiers. IEEE Access 9, 134899\u2013134915 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3116657","DOI":"10.1109\/ACCESS.2021.3116657"},{"key":"1245_CR18","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS\u201917, pp. 6000\u20136010. Curran Associates Inc, Red Hook, NY, USA (2017)"},{"key":"1245_CR19","doi-asserted-by":"publisher","unstructured":"Fu, Y., Li, X., Li, Y., Wang, S., Li, D., Liao, J., Zheng, J.: Incorporate opinion-towards for stance detection. Knowledge-Based Systems 246, 108657 (2022) https:\/\/doi.org\/10.1016\/j.knosys.2022.108657","DOI":"10.1016\/j.knosys.2022.108657"},{"key":"1245_CR20","doi-asserted-by":"publisher","unstructured":"Li, Y., Caragea, C.: Multi-task stance detection with sentiment and stance lexicons. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6299\u20136305. Association for Computational Linguistics, Hong Kong, China (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1657 . https:\/\/aclanthology.org\/D19-1657\/","DOI":"10.18653\/v1\/D19-1657"},{"key":"1245_CR21","doi-asserted-by":"crossref","unstructured":"Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Liu, T., Chang, B., Sun, X., Li, L., Sui, Z.: A Survey on In-context Learning (2024). arXiv: 2301.00234","DOI":"10.18653\/v1\/2024.emnlp-main.64"},{"key":"1245_CR22","unstructured":"Zhao, Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate before use: Improving few-shot performance of language models. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR, vol. 139, pp. 12697\u201312706. (2021)https:\/\/proceedings.mlr.press\/v139\/zhao21c.html"},{"key":"1245_CR23","unstructured":"Su, H., Kasai, J., Wu, C.H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N.A., Yu, T.: Selective Annotation Makes Language Models Better Few-Shot Learners (2022). arXiv: 2209.01975"},{"key":"1245_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, Y., Cheng, S., Li, P., Liu, Y.: DEEM: Dynamic experienced expert modeling for stance detection. In: Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., Xue, N. (eds.) Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 4530\u20134541. ELRA and ICCL, Torino, Italia (2024). https:\/\/aclanthology.org\/2024.lrec-main.405\/","DOI":"10.63317\/2cpy9uhwmbn8"},{"key":"1245_CR25","doi-asserted-by":"publisher","unstructured":"Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., Ji, H.: Unleashing the emergent cognitive synergy in large language models: A task-solving agent through multi-persona self-collaboration. In: Duh, K., Gomez, H., Bethard, S. (eds.) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 257\u2013279. Association for Computational Linguistics, Mexico City, Mexico (2024). https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.15 . https:\/\/aclanthology.org\/2024.naacl-long.15\/","DOI":"10.18653\/v1\/2024.naacl-long.15"},{"key":"1245_CR26","doi-asserted-by":"crossref","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large Language Models are Zero-Shot Reasoners (2023). arXiv: 2205.11916","DOI":"10.52202\/068431-1613"},{"key":"1245_CR27","doi-asserted-by":"publisher","unstructured":"Tan, C., Xu, X., Shen, F.: A survey of zero shot detection: Methods and applications. Cognitive Robotics 1, 159\u2013167 (2021). https:\/\/doi.org\/10.1016\/j.cogr.2021.08.001","DOI":"10.1016\/j.cogr.2021.08.001"},{"key":"1245_CR28","doi-asserted-by":"publisher","unstructured":"Nguyen, Q.M., Kim, T.: Is external information useful for stance detection with LLMs? In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T. (eds.) Findings of the Association for Computational Linguistics: ACL 2025, pp. 14798\u201314807. Association for Computational Linguistics, Vienna, Austria (2025). https:\/\/doi.org\/10.18653\/v1\/2025.findings-acl.764 . https:\/\/aclanthology.org\/2025.findings-acl.764\/","DOI":"10.18653\/v1\/2025.findings-acl.764"},{"key":"1245_CR29","doi-asserted-by":"publisher","unstructured":"Aiyappa, R., Senthilmani, S., An, J., Kwak, H., Ahn, Y.-Y.: Zero-shot stance detection in practice: insights on training, prompting, and decoding with a capable lightweight llm. PeerJ Computer Science 12, 3540 (2026) https:\/\/doi.org\/10.7717\/peerj-cs.3540","DOI":"10.7717\/peerj-cs.3540"},{"key":"1245_CR30","doi-asserted-by":"publisher","unstructured":"Cruickshank, I.J., Ng, L.H.X.: Prompting and fine-tuning open-sourced large language models for stance classification. ACM Trans. Intell. Syst. Technol. (2025). https:\/\/doi.org\/10.1145\/3725816. Just Accepted","DOI":"10.1145\/3725816"},{"key":"1245_CR31","unstructured":"Zhang, Y., Yuan, Y., Yao, A.C.-C.: Meta Prompting for AI Systems (2025). https:\/\/arxiv.org\/abs\/2311.11482"},{"key":"1245_CR32","unstructured":"Suzgun, M., Kalai, A.T.: Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding (2024). arXiv: 2401.12954"},{"key":"1245_CR33","doi-asserted-by":"publisher","unstructured":"Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N.A., Khashabi, D., Hajishirzi, H.: Self-instruct: Aligning language models with self-generated instructions. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13484\u201313508. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.754 . https:\/\/aclanthology.org\/2023.acl-long.754\/","DOI":"10.18653\/v1\/2023.acl-long.754"},{"key":"1245_CR34","doi-asserted-by":"publisher","unstructured":"Peng, K., Ding, L., Yuan, Y., Liu, X., Zhang, M., Ouyang, Y., Tao, D.: Revisiting demonstration selection strategies in in-context learning. In: Ku, L.-W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9090\u20139101. Association for Computational Linguistics, Bangkok, Thailand (2024). https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.492 . https:\/\/aclanthology.org\/2024.acl-long.492\/","DOI":"10.18653\/v1\/2024.acl-long.492"},{"key":"1245_CR35","doi-asserted-by":"publisher","unstructured":"Nguyen, D.Q., Vu, T., Tuan\u00a0Nguyen, A.: BERTweet: A pre-trained language model for English tweets. In: Liu, Q., Schlangen, D. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 9\u201314. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-demos.2 . https:\/\/aclanthology.org\/2020.emnlp-demos.2\/","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"1245_CR36","doi-asserted-by":"publisher","unstructured":"Barbieri, F., Camacho-Collados, J., Espinosa\u00a0Anke, L., Neves, L.: TweetEval: Unified benchmark and comparative evaluation for tweet classification. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1644\u20131650. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.148 . https:\/\/aclanthology.org\/2020.findings-emnlp.148","DOI":"10.18653\/v1\/2020.findings-emnlp.148"},{"key":"1245_CR37","unstructured":"Zhuang, L., Wayne, L., Ya, S., Jun, Z.: A robustly optimized BERT pre-training approach with post-training. In: Li, S., Sun, M., Liu, Y., Wu, H., Liu, K., Che, W., He, S., Rao, G. (eds.) Proceedings of the 20th Chinese National Conference on Computational Linguistics, pp. 1218\u20131227. Chinese Information Processing Society of China, Huhhot, China (2021). https:\/\/aclanthology.org\/2021.ccl-1.108\/"},{"key":"1245_CR38","doi-asserted-by":"publisher","unstructured":"Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: SemEval-2016 task 6: Detecting stance in tweets. In: Bethard, S., Carpuat, M., Cer, D., Jurgens, D., Nakov, P., Zesch, T. (eds.) Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 31\u201341. Association for Computational Linguistics, San Diego, California (2016). https:\/\/doi.org\/10.18653\/v1\/S16-1003 . https:\/\/aclanthology.org\/S16-1003\/","DOI":"10.18653\/v1\/S16-1003"},{"key":"1245_CR39","doi-asserted-by":"publisher","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: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2355\u20132365. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.208 . https:\/\/aclanthology.org\/2021.findings-acl.208\/","DOI":"10.18653\/v1\/2021.findings-acl.208"},{"key":"1245_CR40","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. NIPS \u201922, Curran Associates Inc, Red Hook, NY, USA (2022)","DOI":"10.52202\/068431-1800"},{"key":"1245_CR41","unstructured":"Zhang, B., Fu, X., Ding, D., Huang, H., Dai, G., Yin, N., Li, Y., Jing, L.: Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media (2024). arXiv: 2304.03087"},{"key":"1245_CR42","doi-asserted-by":"crossref","unstructured":"Liyanage, C., Gokani, R., Mago, V.: Gpt-4 as a twitter data annotator: Unraveling its performance on a stance classification task, (2023). https:\/\/doi.org\/10.36227\/techrxiv.24143706.v1","DOI":"10.36227\/techrxiv.24143706.v1"},{"key":"1245_CR43","unstructured":"Jiang, A.Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D.S., Casas, D., Hanna, E.B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L.R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T.L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mixtral of Experts (2024). arXiv: 2401.04088"},{"key":"1245_CR44","doi-asserted-by":"publisher","unstructured":"Aggarwal, D., Sathe, A., Watts, I., Sitaram, S.: MAPLE: Multilingual evaluation of parameter efficient finetuning of large language models. In: Ku, L.-W., Martins, A., Srikumar, V. (eds.) Findings of the Association for Computational Linguistics: ACL 2024, pp. 14824\u201314867. Association for Computational Linguistics, Bangkok, Thailand (2024). https:\/\/doi.org\/10.18653\/v1\/2024.findings-acl.881 . https:\/\/aclanthology.org\/2024.findings-acl.881\/","DOI":"10.18653\/v1\/2024.findings-acl.881"},{"key":"1245_CR45","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1) (2020)"},{"key":"1245_CR46","doi-asserted-by":"publisher","unstructured":"Liang, B., Fu, Y., Gui, L., Yang, M., Du, J., He, Y., Xu, R.: Target-adaptive graph for cross-target stance detection. In: Proceedings of the Web Conference 2021. WWW \u201921, pp. 3453\u20133464. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3442381.3449790","DOI":"10.1145\/3442381.3449790"},{"key":"1245_CR47","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, K., Xian, Y.: Cross-target stance detection with multi-level information fusion. IEICE Transactions on Information and Systems E108.D(8), 947\u2013957 (2025) https:\/\/doi.org\/10.1587\/transinf.2024EDP7303","DOI":"10.1587\/transinf.2024EDP7303"},{"key":"1245_CR48","doi-asserted-by":"publisher","unstructured":"Liang, B., Chen, Z., Gui, L., He, Y., Yang, M., Xu, R.: Zero-shot stance detection via contrastive learning. In: Proceedings of the ACM Web Conference 2022. WWW \u201922, pp. 2738\u20132747. Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3485447.3511994","DOI":"10.1145\/3485447.3511994"},{"key":"1245_CR49","doi-asserted-by":"publisher","unstructured":"Xu, C., Paris, C., Nepal, S., Sparks, R.: Cross-target stance classification with self-attention networks. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 778\u2013783. Association for Computational Linguistics, Melbourne, Australia (2018). https:\/\/doi.org\/10.18653\/v1\/P18-2123 . https:\/\/aclanthology.org\/P18-2123\/","DOI":"10.18653\/v1\/P18-2123"},{"key":"1245_CR50","doi-asserted-by":"publisher","unstructured":"He, Z., Mokhberian, N., Lerman, K.: Infusing knowledge from Wikipedia to enhance stance detection. In: Barnes, J., De\u00a0Clercq, O., Barriere, V., Tafreshi, S., Alqahtani, S., Sedoc, J., Klinger, R., Balahur, A. (eds.) Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pp. 71\u201377. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.wassa-1.7 . https:\/\/aclanthology.org\/2022.wassa-1.7\/","DOI":"10.18653\/v1\/2022.wassa-1.7"},{"key":"1245_CR51","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Li, Y., Zhang, J., Xu, H.: LLM-driven knowledge injection advances zero-shot and cross-target stance detection. In: Duh, K., Gomez, H., Bethard, S. (eds.) 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. Association for Computational Linguistics, Mexico City, Mexico (2024). https:\/\/doi.org\/10.18653\/v1\/2024.naacl-short.32. https:\/\/aclanthology.org\/2024.naacl-short.32\/","DOI":"10.18653\/v1\/2024.naacl-short.32"},{"key":"1245_CR52","doi-asserted-by":"publisher","unstructured":"Tang, H., Ji, D., Li, C., Zhou, Q.: Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6578\u20136588. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.588. https:\/\/aclanthology.org\/2020.acl-main.588\/","DOI":"10.18653\/v1\/2020.acl-main.588"},{"key":"1245_CR53","doi-asserted-by":"publisher","unstructured":"Chen, C., Teng, Z., Wang, Z., Zhang, Y.: Discrete opinion tree induction for aspect-based sentiment analysis. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2051\u20132064. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.145 . https:\/\/aclanthology.org\/2022.acl-long.145\/","DOI":"10.18653\/v1\/2022.acl-long.145"},{"key":"1245_CR54","doi-asserted-by":"publisher","unstructured":"Yang, J., Yang, R., Lu, H., Wang, C., Xie, J.: Multi-entity aspect-based sentiment analysis with context, entity, aspect memory and dependency information. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 18(4) (2019) https:\/\/doi.org\/10.1145\/3321125","DOI":"10.1145\/3321125"},{"key":"1245_CR55","doi-asserted-by":"publisher","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Nakov, P., Zesch, T. (eds.) Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27\u201335. Association for Computational Linguistics, Dublin, Ireland (2014). https:\/\/doi.org\/10.3115\/v1\/S14-2004 . https:\/\/aclanthology.org\/S14-2004\/","DOI":"10.3115\/v1\/S14-2004"},{"key":"1245_CR56","doi-asserted-by":"publisher","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 task 12: Aspect based sentiment analysis. In: Nakov, P., Zesch, T., Cer, D., Jurgens, D. (eds.) Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486\u2013495. Association for Computational Linguistics, Denver, Colorado (2015). https:\/\/doi.org\/10.18653\/v1\/S15-2082 . https:\/\/aclanthology.org\/S15-2082\/","DOI":"10.18653\/v1\/S15-2082"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-026-01245-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-026-01245-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-026-01245-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T18:39:42Z","timestamp":1786991982000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-026-01245-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,17]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["1245"],"URL":"https:\/\/doi.org\/10.1007\/s41060-026-01245-z","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,17]]},"assertion":[{"value":"14 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable","order":1,"name":"Ethics","label":"Ethical Approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","label":"Conflicts of Interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"277"}}