{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:01:06Z","timestamp":1784696466769,"version":"3.55.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"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":"crossref","award":["72171229"],"award-info":[{"award-number":["72171229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02844-9","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:03:12Z","timestamp":1784692992000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint aspect-based sentiment and overall rating prediction via a cross-modal transformer in user reviews"],"prefix":"10.1007","volume":"68","author":[{"given":"Xiaoning","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"issue":"1\u20132","key":"2844_CR1","first-page":"1","volume":"2","author":"B Pang","year":"2008","unstructured":"Pang B, Lee L et al (2008) Opinion mining and sentiment analysis. Found Trends\u00ae Inform 2(1\u20132):1\u2013135","journal-title":"Found Trends\u00ae Inform"},{"key":"2844_CR2","volume-title":"Sentiment analysis and opinion mining","author":"B Liu","year":"2022","unstructured":"Liu B (2022) Sentiment analysis and opinion mining. Springer, Cham"},{"key":"2844_CR3","doi-asserted-by":"publisher","DOI":"10.1017\/9781108639286","volume-title":"Sentiment analysis: mining opinions, sentiments, and emotions","author":"B Liu","year":"2020","unstructured":"Liu B (2020) Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge"},{"key":"2844_CR4","doi-asserted-by":"crossref","unstructured":"Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 168\u2013177","DOI":"10.1145\/1014052.1014073"},{"issue":"4","key":"2844_CR5","doi-asserted-by":"publisher","first-page":"103728","DOI":"10.1016\/j.ipm.2024.103728","volume":"61","author":"P Wu","year":"2024","unstructured":"Wu P, Tang T, Zhou L, Mart\u00ednez L (2024) A decision-support model through online reviews: consumer preference analysis and product ranking. Inf Process Manag 61(4):103728","journal-title":"Inf Process Manag"},{"key":"2844_CR6","doi-asserted-by":"crossref","unstructured":"Seo S, Huang J, Yang H, Liu Y (2017) Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the eleventh ACM conference on recommender systems, pp. 297\u2013305","DOI":"10.1145\/3109859.3109890"},{"key":"2844_CR7","unstructured":"Li F, Liu NN, Jin H, Zhao K, Yang Q, Zhu X (2011) Incorporating reviewer and product information for review rating prediction. In: Twenty-second international joint conference on artificial intelligence"},{"key":"2844_CR8","unstructured":"Tang D, Qin B, Liu T, Yang Y (2015) User modeling with neural network for review rating prediction. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"2844_CR9","first-page":"2986","volume":"16","author":"W Zhang","year":"2016","unstructured":"Zhang W, Yuan Q, Han J, Wang J (2016) Collaborative multi-level embedding learning from reviews for rating prediction. IJCAI 16:2986\u20132992","journal-title":"IJCAI"},{"key":"2844_CR10","doi-asserted-by":"crossref","unstructured":"Bu J, Ren L, Zheng S, Yang Y, Wang J, Zhang F, Wu W (2021) Asap: a chinese review dataset towards aspect category sentiment analysis and rating prediction. arXiv preprint arXiv:2103.06605","DOI":"10.18653\/v1\/2021.naacl-main.167"},{"key":"2844_CR11","doi-asserted-by":"crossref","unstructured":"You Q, Luo J, Jin H, Yang J (2016) Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia. In: Proceedings of the ninth ACM international conference on web search and data mining, pp. 13\u201322","DOI":"10.1145\/2835776.2835779"},{"issue":"3","key":"2844_CR12","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.im.2014.12.008","volume":"52","author":"C Luo","year":"2015","unstructured":"Luo C, Luo XR, Xu Y, Warkentin M, Sia CL (2015) Examining the moderating role of sense of membership in online review evaluations. Inf Manag 52(3):305\u2013316","journal-title":"Inf Manag"},{"key":"2844_CR13","doi-asserted-by":"crossref","unstructured":"Truong Q-T, Lauw HW (2017) Visual sentiment analysis for review images with item-oriented and user-oriented CNN. In: Proceedings of the 25th ACM international conference on multimedia, pp. 1274\u20131282","DOI":"10.1145\/3123266.3123374"},{"key":"2844_CR14","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10462-020-09892-9","volume":"54","author":"ZY Khan","year":"2021","unstructured":"Khan ZY, Niu Z, Sandiwarno S, Prince R (2021) Deep learning techniques for rating prediction: a survey of the state-of-the-art. Artif Intell Rev 54:95\u2013135","journal-title":"Artif Intell Rev"},{"key":"2844_CR15","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1016\/j.ejor.2024.01.042","volume":"316","author":"Y Xiong","year":"2024","unstructured":"Xiong Y, Liu Y, Qian Y, Jiang Y, Chai Y, Ling H (2024) Review-based recommendation under preference uncertainty: an asymmetric deep learning framework. Eur J Oper Res 316:1044\u20131057","journal-title":"Eur J Oper Res"},{"issue":"6","key":"2844_CR16","first-page":"1059","volume":"40","author":"PS Dhillon","year":"2021","unstructured":"Dhillon PS, Aral S (2021) Modeling dynamic user interests: a neural matrix factorization approach. Mark Sci 40(6):1059\u20131080","journal-title":"Mark Sci"},{"key":"2844_CR17","doi-asserted-by":"publisher","first-page":"114088","DOI":"10.1016\/j.dss.2023.114088","volume":"177","author":"F Zhou","year":"2024","unstructured":"Zhou F, Jiang Y, Qian Y, Liu Y, Chai Y (2024) Product consumptions meet reviews: inferring consumer preferences by an explainable machine learning approach. Decis Support Syst 177:114088","journal-title":"Decis Support Syst"},{"key":"2844_CR18","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/s13042-020-01181-9","volume":"12","author":"S Feng","year":"2021","unstructured":"Feng S, Song K, Wang D, Gao W, Zhang Y (2021) Intersentiment: combining deep neural models on interaction and sentiment for review rating prediction. Int J Mach Learn Cybern 12:477\u2013488","journal-title":"Int J Mach Learn Cybern"},{"key":"2844_CR19","doi-asserted-by":"crossref","unstructured":"Wang X, Xiao T, Tan J, Ouyang D, Shao J (2020) Mrmrp: multi-source review-based model for rating prediction. In: Database systems for advanced applications: 25th international conference (DASFAA 2020). Springer, Cham","DOI":"10.1007\/978-3-030-59416-9_2"},{"issue":"1","key":"2844_CR20","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1287\/isre.2021.1036","volume":"33","author":"K Bauman","year":"2022","unstructured":"Bauman K, Tuzhilin A (2022) Know thy context: parsing contextual information from user reviews for recommendation purposes. Inf Syst Res 33(1):179\u2013202","journal-title":"Inf Syst Res"},{"key":"2844_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-024-02104-8","author":"IA Kandhro","year":"2024","unstructured":"Kandhro IA, Ali F, Uddin M, Kehar A, Manickam S (2024) Exploring aspect-based sentiment analysis: an in-depth review of current methods and prospects for advancement. Knowl Inf Syst. https:\/\/doi.org\/10.1007\/s10115-024-02104-8","journal-title":"Knowl Inf Syst"},{"key":"2844_CR22","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Liu J, Huang Y, Xie X (2019) Arp: Aspect-aware neural review rating prediction. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp. 2169\u20132172","DOI":"10.1145\/3357384.3358086"},{"key":"2844_CR23","doi-asserted-by":"crossref","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short Papers), pp. 207\u2013212","DOI":"10.18653\/v1\/P16-2034"},{"key":"2844_CR24","doi-asserted-by":"publisher","unstructured":"Long Y, Lu Q, Xiang R, Li M, Huang CR (2017) A cognition based attention model for sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp. 462\u2013471. Association for Computational Linguistics, Copenhagen, Denmark. https:\/\/doi.org\/10.18653\/v1\/D17-1048, https:\/\/aclanthology.org\/D17-1048\/","DOI":"10.18653\/v1\/D17-1048"},{"issue":"1","key":"2844_CR25","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Comput 3(1):79\u201387","journal-title":"Neural Comput"},{"key":"2844_CR26","doi-asserted-by":"crossref","unstructured":"Ma J, Zhao Z, Yi X, Chen J, Hong L, Chi EH (2018) Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1930\u20131939","DOI":"10.1145\/3219819.3220007"},{"key":"2844_CR27","doi-asserted-by":"crossref","unstructured":"Xu N, Mao W, Chen G (2019) Multi-interactive memory network for aspect based multimodal sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 371\u2013378","DOI":"10.1609\/aaai.v33i01.3301371"},{"key":"2844_CR28","doi-asserted-by":"crossref","unstructured":"Xu N, Mao W (2017) Multisentinet: a deep semantic network for multimodal sentiment analysis. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 2399\u20132402","DOI":"10.1145\/3132847.3133142"},{"key":"2844_CR29","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2015) Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 1422\u20131432","DOI":"10.18653\/v1\/D15-1167"},{"key":"2844_CR30","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp. 1480\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"2844_CR31","unstructured":"Lin Z, Liang B, Long Y, Dang Y, Yang M, Zhang M, Xu R (2022) Modeling intra-and inter-modal relations: Hierarchical graph contrastive learning for multimodal sentiment analysis. In: Proceedings of the 29th international conference on computational linguistics, vol 29. Association for Computational Linguistics, Berkeley"},{"key":"2844_CR32","doi-asserted-by":"publisher","first-page":"103538","DOI":"10.1016\/j.ipm.2023.103538","volume":"61","author":"Q Lu","year":"2024","unstructured":"Lu Q, Sun X, Gao Z, Long Y, Feng J, Zhang H (2024) Coordinated-joint translation fusion framework with sentiment-interactive graph convolutional networks for multimodal sentiment analysis. Inf Process Manag 61:103538","journal-title":"Inf Process Manag"},{"key":"2844_CR33","doi-asserted-by":"publisher","first-page":"6879","DOI":"10.1007\/s10115-025-02446-x","volume":"67","author":"B Yu","year":"2025","unstructured":"Yu B, Li C, Shi Z (2025) Multi-grained feature gating fusion network for multimodal sentiment analysis. Knowl Inf Syst 67:6879\u20136905","journal-title":"Knowl Inf Syst"},{"key":"2844_CR34","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ijhm.2017.12.008","volume":"71","author":"Y Ma","year":"2018","unstructured":"Ma Y, Xiang Z, Du Q, Fan W (2018) Effects of user-provided photos on hotel review helpfulness: an analytical approach with deep leaning. Int J Hosp Manag 71:120\u2013131","journal-title":"Int J Hosp Manag"},{"key":"2844_CR35","doi-asserted-by":"crossref","unstructured":"Hu A, Flaxman S (2018) Multimodal sentiment analysis to explore the structure of emotions. In: Proceedings of the 24th ACM sigkdd international conference on knowledge discovery & data mining, pp. 350\u2013358","DOI":"10.1145\/3219819.3219853"},{"key":"2844_CR36","doi-asserted-by":"crossref","unstructured":"Yu Z, Yu J, Fan J, Tao D (2017) Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: Proceedings of the IEEE international conference on computer vision, pp. 1821\u20131830","DOI":"10.1109\/ICCV.2017.202"},{"issue":"12","key":"2844_CR37","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.1109\/TNNLS.2018.2817340","volume":"29","author":"Z Yu","year":"2018","unstructured":"Yu Z, Yu J, Xiang C, Fan J, Tao D (2018) Beyond bilinear: generalized multimodal factorized high-order pooling for visual question answering. IEEE Trans Neural Netw Learn Syst 29(12):5947\u20135959","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2844_CR38","doi-asserted-by":"crossref","unstructured":"Fukui A, Park DH, Yang D, Rohrbach A, Darrell T, Rohrbach M (2016) Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv preprint arXiv:1606.01847","DOI":"10.18653\/v1\/D16-1044"},{"key":"2844_CR39","unstructured":"Kim J-H, On K-W, Lim W, Kim J, Ha J-W, Zhang B-T (2016) Hadamard product for low-rank bilinear pooling. arXiv preprint arXiv:1610.04325"},{"key":"2844_CR40","doi-asserted-by":"crossref","unstructured":"Truong Q-T, Lauw HW (2019) Vistanet: visual aspect attention network for multimodal sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 305\u2013312","DOI":"10.1609\/aaai.v33i01.3301305"},{"key":"2844_CR41","doi-asserted-by":"crossref","unstructured":"Tsai Y-HH, Bai S, Liang PP, Kolter JZ, Morency L-P, Salakhutdinov R (2019) Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the conference. Association for computational linguistics. Meeting, vol. 2019, p. 6558. NIH Public Access","DOI":"10.18653\/v1\/P19-1656"},{"key":"2844_CR42","doi-asserted-by":"crossref","unstructured":"Yu J, Wang J, Xia R, Li J (2022) Targeted multimodal sentiment classification based on coarse-to-fine grained image-target matching. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, pp. 4482\u20134488","DOI":"10.24963\/ijcai.2022\/622"},{"key":"2844_CR43","doi-asserted-by":"publisher","first-page":"103878","DOI":"10.1016\/j.inffus.2025.103878","volume":"127","author":"Y Lin","year":"2025","unstructured":"Lin Y, Wang Z, Qin G, Wu L, Li Y (2025) Semantic-guided multi-grained cross-modal alignment and fusion network for multimodal aspect-based sentiment analysis. Inf Fusion 127:103878","journal-title":"Inf Fusion"},{"key":"2844_CR44","doi-asserted-by":"crossref","unstructured":"Feng X, Lin Y, He L, Li Y, Chang L, Zhou Y (2024) Knowledge-guided dynamic modality attention fusion framework for multimodal sentiment analysis. In: Findings of the association for computational linguistics: EMNLP 2024, pp. 14755\u201314766","DOI":"10.18653\/v1\/2024.findings-emnlp.865"},{"issue":"4","key":"2844_CR45","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s10462-023-10685-z","volume":"57","author":"Y Li","year":"2024","unstructured":"Li Y, Ding H, Lin Y, Feng X, Chang L (2024) Multi-level textual-visual alignment and fusion network for multimodal aspect-based sentiment analysis. Artif Intell Rev 57(4):78","journal-title":"Artif Intell Rev"},{"key":"2844_CR46","doi-asserted-by":"publisher","first-page":"5125","DOI":"10.1007\/s10115-025-02372-y","volume":"67","author":"B Yu","year":"2025","unstructured":"Yu B, Xing Y, Yang Y, Cao C, Shi Z (2025) Multimodal aspect-based sentiment analysis based on a dual syntactic graph network and joint contrastive learning. Knowl Inf Syst 67:5125\u20135149","journal-title":"Knowl Inf Syst"},{"key":"2844_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.131985","author":"L He","year":"2025","unstructured":"He L, Lin Y, Qin G, Liu J, Feng X, Zhou Y (2025) Dual dynamic multi-granularity fusion method for multimodal sarcasm detection. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2025.131985","journal-title":"Neurocomputing"},{"key":"2844_CR48","doi-asserted-by":"crossref","unstructured":"Zhang W, et al (2024) Sentiment analysis in the era of large language models. In: Findings of the association for computational linguistics: NAACL 2024. https:\/\/aclanthology.org\/2024.findings-naacl.246\/","DOI":"10.18653\/v1\/2024.findings-naacl.246"},{"key":"2844_CR49","unstructured":"Simmering PF, et al (2023) Large language models for aspect-based sentiment analysis. arXiv preprint arXiv:2310.18025 [cs.CL]"},{"key":"2844_CR50","unstructured":"OpenAI: Gpt-4 technical report. arXiv preprint (2023) arXiv:2303.08774 [cs.CL]"},{"key":"2844_CR51","unstructured":"Li J, Li D, Savarese S, Hoi S (2023) Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: Proceedings of the 40th international conference on machine learning (ICML). Proceedings of Machine Learning Research, vol. 202"},{"key":"2844_CR52","doi-asserted-by":"crossref","unstructured":"Dai W, Li J, Li D, Tiong AMH, Zhao J, Wang W, Li B, Fung P, Hoi S (2023) Instructblip: towards general-purpose vision-language models with instruction tuning. In: Advances in neural information processing systems (NeurIPS)","DOI":"10.52202\/075280-2142"},{"key":"2844_CR53","doi-asserted-by":"crossref","unstructured":"Liu H, Li C, Wu Q, Lee YJ (2023) Advances in neural information processing systems (NeurIPS) (Visual instruction tuning)","DOI":"10.52202\/075280-1516"},{"key":"2844_CR54","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"2844_CR55","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2025.3565506","author":"L Xiao","year":"2025","unstructured":"Xiao L, Mao R, Zhao S, Lin Q, Jia Y, He L, Cambria E (2025) Exploring cognitive and aesthetic causality for multimodal aspect-based sentiment analysis. IEEE Trans Affect Comput. https:\/\/doi.org\/10.1109\/TAFFC.2025.3565506","journal-title":"IEEE Trans Affect Comput"},{"key":"2844_CR56","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2844_CR57","doi-asserted-by":"crossref","unstructured":"Ma J, Zhao Z, Yi X, Chen J, Hong L, Chi EH (2018) Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. KDD \u201918, pp. 1930\u20131939. Association for Computing Machinery, New York","DOI":"10.1145\/3219819.3220007"},{"key":"2844_CR58","doi-asserted-by":"crossref","unstructured":"Li D, Zhang Z, Yuan S, Gao M, Zhang W, Yang C, Liu X, Yang J (2023) Adatt: adaptive task-to-task fusion network for multitask learning in recommendations. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. KDD \u201923, pp. 4370\u20134379. Association for Computing Machinery, New York","DOI":"10.1145\/3580305.3599769"},{"issue":"1","key":"2844_CR59","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s00530-024-01558-8","volume":"31","author":"QH Nguyen","year":"2025","unstructured":"Nguyen QH, Nguyen M-VT, Van Nguyen K (2025) New benchmark dataset and fine-grained cross-modal fusion framework for Vietnamese multimodal aspect-category sentiment analysis. Multimed Syst 31(1):4","journal-title":"Multimed Syst"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02844-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02844-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02844-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:03:26Z","timestamp":1784693006000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02844-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2844"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02844-9","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]},"assertion":[{"value":"7 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"226"}}