{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:47:33Z","timestamp":1777060053128,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"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":["62176221"],"award-info":[{"award-number":["62176221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52178167"],"award-info":[{"award-number":["52178167"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306247"],"award-info":[{"award-number":["62306247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2024NSFTD0036"],"award-info":[{"award-number":["2024NSFTD0036"]}]},{"name":"Sichuan Science and Technology Program","award":["2024NSFSC1474"],"award-info":[{"award-number":["2024NSFSC1474"]}]},{"name":"The Frontier Cross Innovation Team Project of Southwest Jiaotong University","award":["YH1500112432297"],"award-info":[{"award-number":["YH1500112432297"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M722630"],"award-info":[{"award-number":["2022M722630"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02610-3","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T07:31:19Z","timestamp":1744702279000},"page":"6031-6045","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cross-lingual sentiment analysis empowered by emotional mutual reinforcement through emojis"],"prefix":"10.1007","volume":"16","author":[{"given":"Enping","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azhen","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"2610_CR1","unstructured":"Davidov D, Tsur O, Rappoport A (2010) Enhanced sentiment learning using twitter hashtags and smileys. In: International conference on computational linguistics (COLING), pp 241\u2013249"},{"key":"2610_CR2","doi-asserted-by":"crossref","unstructured":"Gamon M (2004) Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: International conference on computational linguistics (COLING)","DOI":"10.3115\/1220355.1220476"},{"key":"2610_CR3","doi-asserted-by":"crossref","unstructured":"McGlohon M, Glance NS, Reiter Z (2010) Star quality: aggregating reviews to rank products and merchants. In: Proceedings of the international conference on weblogs and social media (ICWSM)","DOI":"10.1609\/icwsm.v4i1.14019"},{"key":"2610_CR4","doi-asserted-by":"crossref","unstructured":"Wu F, Zhang J, Yuan Z, Wu S, Huang Y, Yan J (2017) Sentence-level sentiment classification with weak supervision. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval, pp 973\u2013976","DOI":"10.1145\/3077136.3080693"},{"key":"2610_CR5","doi-asserted-by":"crossref","unstructured":"Liu Q, Zhang H, Zeng Y, Huang Z, Wu Z (2018) Content attention model for aspect based sentiment analysis. In: Proceedings of the world wide web conference on world wide web (WWW), pp 1023\u20131032","DOI":"10.1145\/3178876.3186001"},{"key":"2610_CR6","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.inffus.2021.01.005","volume":"70","author":"H Peng","year":"2021","unstructured":"Peng H, Ma Y, Poria S, Li Y, Cambria E (2021) Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning. Inf Fusion 70:88\u201399","journal-title":"Inf Fusion"},{"issue":"1","key":"2610_CR7","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.csl.2013.04.010","volume":"28","author":"M Ptaszynski","year":"2014","unstructured":"Ptaszynski M, Rzepka R, Araki K, Momouchi Y (2014) Automatically annotating a five-billion-word corpus of Japanese blogs for sentiment and affect analysis. Comput Speech Lang 28(1):38\u201355","journal-title":"Comput Speech Lang"},{"key":"2610_CR8","doi-asserted-by":"crossref","unstructured":"Chen Q, Li C, Li W (2017) Modeling language discrepancy for cross-lingual sentiment analysis. In: Proceedings of the ACM on conference on information and knowledge management (CIKM), pp 117\u2013126","DOI":"10.1145\/3132847.3132915"},{"key":"2610_CR9","doi-asserted-by":"crossref","unstructured":"Barnes J, Klinger R, Walde SS (2018) Bilingual sentiment embeddings: joint projection of sentiment across languages. In: Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 2483\u20132493","DOI":"10.18653\/v1\/P18-1231"},{"key":"2610_CR10","unstructured":"Prettenhofer P, Stein B (2010) Cross-language text classification using structural correspondence learning. In: Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 1118\u20131127"},{"key":"2610_CR11","doi-asserted-by":"crossref","unstructured":"Zhou X, Wan X, Xiao J (2016) Cross-lingual sentiment classification with bilingual document representation learning. In: Proceedings of the annual meeting of the association for computational linguistics (ACL)","DOI":"10.18653\/v1\/P16-1133"},{"key":"2610_CR12","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1613\/jair.4787","volume":"55","author":"SM Mohammad","year":"2016","unstructured":"Mohammad SM, Salameh M, Kiritchenko S (2016) How translation alters sentiment. J Artif Intell Res 55:95\u2013130","journal-title":"J Artif Intell Res"},{"key":"2610_CR13","doi-asserted-by":"crossref","unstructured":"Feng Y, Wan X (2019) Towards a unified end-to-end approach for fully unsupervised cross-lingual sentiment analysis. In: Proceedings of the conference on computational natural language learning (CoNLL), pp 1035\u20131044","DOI":"10.18653\/v1\/K19-1097"},{"key":"2610_CR14","doi-asserted-by":"crossref","unstructured":"Fei H, Li P (2020) Cross-lingual unsupervised sentiment classification with multi-view transfer learning. In: Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 5759\u20135771","DOI":"10.18653\/v1\/2020.acl-main.510"},{"key":"2610_CR15","doi-asserted-by":"crossref","unstructured":"Zhang W, He R, Peng H, Bing L, Lam W (2021) Cross-lingual aspect-based sentiment analysis with aspect term code-switching. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 9220\u20139230","DOI":"10.18653\/v1\/2021.emnlp-main.727"},{"key":"2610_CR16","doi-asserted-by":"crossref","unstructured":"Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm\u00e1n F, Grave E, Ott M, Zettlemoyer L, Stoyanov V (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 8440\u20138451","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"2610_CR17","doi-asserted-by":"crossref","unstructured":"Feng F, Yang Y, Cer D, Arivazhagan N, Wang W (2022) Language-agnostic BERT sentence embedding. In: Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 878\u2013891","DOI":"10.18653\/v1\/2022.acl-long.62"},{"key":"2610_CR18","doi-asserted-by":"crossref","unstructured":"Chen Z, Shen S, Hu Z, Lu X, Mei Q, Liu X (2019) Emoji-powered representation learning for cross-lingual sentiment classification. In: The world wide web conference WWW, pp 251\u2013262","DOI":"10.1145\/3308558.3313600"},{"key":"2610_CR19","doi-asserted-by":"crossref","unstructured":"Lu X, Ai W, Liu X, Li Q, Wang N, Huang G, Mei Q (2016) Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users. In: Proceedings of the international joint conference on pervasive and ubiquitous computing, pp 770\u2013780","DOI":"10.1145\/2971648.2971724"},{"key":"2610_CR20","doi-asserted-by":"crossref","unstructured":"Zhang J, Liang T, Wan M, Yang G, Lv F (2022) Curriculum knowledge distillation for emoji-supervised cross-lingual sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 864\u2013875","DOI":"10.18653\/v1\/2022.emnlp-main.55"},{"issue":"4","key":"2610_CR21","first-page":"246","volume":"2","author":"C Liu","year":"2021","unstructured":"Liu C, Fang F, Lin X, Cai T, Tan X, Liu J, Lu X (2021) Improving sentiment analysis accuracy with emoji embedding. J Saf Sci Resil 2(4):246\u2013252","journal-title":"J Saf Sci Resil"},{"key":"2610_CR22","doi-asserted-by":"crossref","unstructured":"Felbo B, Mislove A, S\u00f8gaard A, Rahwan I, Lehmann S (2017) Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 1615\u20131625","DOI":"10.18653\/v1\/D17-1169"},{"key":"2610_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109924","volume":"257","author":"DS Chauhan","year":"2022","unstructured":"Chauhan DS, Singh GV, Arora A, Ekbal A, Bhattacharyya P (2022) An emoji-aware multitask framework for multimodal sarcasm detection. Knowl Based Syst 257:109924","journal-title":"Knowl Based Syst"},{"key":"2610_CR24","doi-asserted-by":"crossref","unstructured":"Cramer H, Juan P, Tetreault JR (2016) Sender-intended functions of emojis in US messaging. In: Proceedings of the international conference on human-computer interaction with mobile devices and services, pp 504\u2013509","DOI":"10.1145\/2935334.2935370"},{"key":"2610_CR25","doi-asserted-by":"crossref","unstructured":"Miller HJ, Thebault-Spieker J, Chang S, Johnson IL, Terveen LG, Hecht BJ (2016) \u201cblissfully happy\u201d or \u201cready tofight\u201d: varying interpretations of emoji. In: Proceedings of the international conference on web and social media, pp 259\u2013268","DOI":"10.1609\/icwsm.v10i1.14757"},{"key":"2610_CR26","doi-asserted-by":"crossref","unstructured":"Chen Z, Lu X, Ai W, Li H, Mei Q, Liu X (2018) Through a gender lens: learning usage patterns of emojis from large-scale android users. In: Proceedings of the world wide web conference on world wide web WWW, pp 763\u2013772","DOI":"10.1145\/3178876.3186157"},{"key":"2610_CR27","doi-asserted-by":"crossref","unstructured":"Barbieri F, Kruszewski G, Ronzano F, Saggion H (2016) How cosmopolitan are emojis?: Exploring emojis usage and meaning over different languages with distributional semantics. In: Proceedings of the ACM conference on multimedia conference, (MM), pp 531\u2013535","DOI":"10.1145\/2964284.2967278"},{"key":"2610_CR28","doi-asserted-by":"crossref","unstructured":"Hu T, Guo H, Sun H, Nguyen TT, Luo J (2017) Spice up your chat: the intentions and sentiment effects of using emojis. In: Proceedings of the eleventh international conference on web and social media (ICWSM), pp 102\u2013111","DOI":"10.1609\/icwsm.v11i1.14869"},{"key":"2610_CR29","doi-asserted-by":"crossref","unstructured":"Felbo B, Mislove A, S\u00f8gaard A, Rahwan I, Lehmann S (2017) Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 1615\u20131625","DOI":"10.18653\/v1\/D17-1169"},{"key":"2610_CR30","unstructured":"Yin W, Alkhalifa R, Zubiaga A (2021) The emojification of sentiment on social media: Collection and analysis of a longitudinal twitter sentiment dataset. CoRR arXiv:2108:13898"},{"issue":"7","key":"2610_CR31","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1145\/3645106","volume":"56","author":"C Zhao","year":"2024","unstructured":"Zhao C, Wu M, Yang X, Zhang W, Zhang S, Wang S, Li D (2024) A systematic review of cross-lingual sentiment analysis: tasks, strategies, and prospects. ACM Comput Surv 56(7):177\u2013117737","journal-title":"ACM Comput Surv"},{"key":"2610_CR32","doi-asserted-by":"crossref","unstructured":"Jefry W, Al-Doghman F, Hussain F (2023) Comparison of artificial intelligence models in cross-lingual transfer learning through sentiment analysis. In: IEEE international conference on e-business engineering, ICEBE, pp 16\u201322","DOI":"10.1109\/ICEBE59045.2023.00021"},{"key":"2610_CR33","doi-asserted-by":"crossref","unstructured":"Fei H, Li P (2020) Cross-lingual unsupervised sentiment classification with multi-view transfer learning. In: Proceedings of the annual meeting of the association for computational linguisticsj (ACL), pp 5759\u20135771","DOI":"10.18653\/v1\/2020.acl-main.510"},{"key":"2610_CR34","doi-asserted-by":"crossref","unstructured":"Al-Shabi A, Adel A, Omar N, Al-Moslmi T (2017) Cross-lingual sentiment classification from english to arabic using machine translation. Int J Adv Comput Sci Appl 8(12)","DOI":"10.14569\/IJACSA.2017.081257"},{"key":"2610_CR35","doi-asserted-by":"crossref","unstructured":"Xiao M, Guo Y (2013) Semi-supervised representation learning for cross-lingual text classification. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 1465\u20131475","DOI":"10.18653\/v1\/D13-1153"},{"key":"2610_CR36","doi-asserted-by":"crossref","unstructured":"Pham H, Luong T, Manning CD (2015) Learning distributed representations for multilingual text sequences. In: Proceedings of the workshop on vector space modeling for natural language processing, pp 88\u201394","DOI":"10.3115\/v1\/W15-1512"},{"key":"2610_CR37","doi-asserted-by":"crossref","unstructured":"Xu R, Yang Y (2017) Cross-lingual distillation for text classification. In: Barzilay R, Kan M (eds) Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 1415\u20131425","DOI":"10.18653\/v1\/P17-1130"},{"key":"2610_CR38","doi-asserted-by":"crossref","unstructured":"Xu K, Wan X (2017) Towards a universal sentiment classifier in multiple languages. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 511\u2013520","DOI":"10.18653\/v1\/D17-1053"},{"key":"2610_CR39","doi-asserted-by":"crossref","unstructured":"Ziser Y, Reichart R (2018) Deep pivot-based modeling for cross-language cross-domain transfer with minimal guidance. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 238\u2013249","DOI":"10.18653\/v1\/D18-1022"},{"key":"2610_CR40","doi-asserted-by":"crossref","unstructured":"Chen Z, Qian T (2019) Transfer capsule network for aspect level sentiment classification. In: Proceedings of the conference of the association for computational linguistics (ACL), pp 547\u2013556","DOI":"10.18653\/v1\/P19-1052"},{"issue":"12","key":"2610_CR41","first-page":"254","volume":"22","author":"VJ Prakash","year":"2023","unstructured":"Prakash VJ, Vijay SAA (2023) Cross-lingual sentiment analysis of Tamil language using a multi-stage deep learning architecture. ACM Trans Asian Low Resour Lang Inf Process 22(12):254\u2013125428","journal-title":"ACM Trans Asian Low Resour Lang Inf Process"},{"key":"2610_CR42","first-page":"82","volume":"14302","author":"Y Xu","year":"2023","unstructured":"Xu Y, Du W, Hu L (2023) A cross-lingual sentiment embedding model with semantic and sentiment joint learning. Nat Lang Process Chin Comput 14302:82\u201394","journal-title":"Nat Lang Process Chin Comput"},{"issue":"1","key":"2610_CR43","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1038\/s41598-024-60210-7","volume":"14","author":"MSU Miah","year":"2024","unstructured":"Miah MSU, Kabir MM, Sarwar TB, Safran M, Alfarhood S, Mridha M (2024) A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and llm. Sci Rep 14(1):9603","journal-title":"Sci Rep"},{"key":"2610_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111553","volume":"157","author":"A Ag","year":"2024","unstructured":"Ag A, Vetriselvi V (2024) Sentiment analysis on a low-resource language dataset using multimodal representation learning and cross-lingual transfer learning. Appl Soft Comput 157:111553","journal-title":"Appl Soft Comput"},{"key":"2610_CR45","unstructured":"Jahan RI, Fan H, Chen H, Feng Y (2024) Unlocking cross-lingual sentiment analysis through emoji interpretation: a multimodal generative ai approach. arXiv:2412.17255"},{"key":"2610_CR46","doi-asserted-by":"crossref","unstructured":"Miller HJ, Thebault-Spieker J, Chang S, Johnson IL, Terveen LG, Hecht BJ (2016) \u201cblissfully happy\u201d or \u201cready tofight\u201d: varying interpretations of emoji. In: Proceedings of the international conference on web and social media, pp 259\u2013268","DOI":"10.1609\/icwsm.v10i1.14757"},{"key":"2610_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108437","volume":"242","author":"S Lee","year":"2022","unstructured":"Lee S, Jeong D, Park E (2022) Multiemo: multi-task framework for emoji prediction. Knowl Based Syst 242:108437","journal-title":"Knowl Based Syst"},{"issue":"3","key":"2610_CR48","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s13042-021-01356-y","volume":"13","author":"R Yan","year":"2022","unstructured":"Yan R, Yu Y, Qiu D (2022) Emotion-enhanced classification based on fuzzy reasoning. Int J Mach Learn Cybern 13(3):839\u2013850","journal-title":"Int J Mach Learn Cybern"},{"issue":"5","key":"2610_CR49","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1145\/3389035","volume":"19","author":"Y Lou","year":"2020","unstructured":"Lou Y, Zhang Y, Li F, Qian T, Ji D (2020) Emoji-based sentiment analysis using attention networks. ACM Trans Asian Low Resour Lang Inf Process 19(5):64\u201316413","journal-title":"ACM Trans Asian Low Resour Lang Inf Process"},{"key":"2610_CR50","doi-asserted-by":"crossref","unstructured":"Yuan X, Hu J, Zhang X, Lv H (2022) Pay attention to emoji: feature fusion network with emograph2vec model for sentiment analysis. In: International conference on pattern recognition (ICPR), pp 1529\u20131535","DOI":"10.1109\/ICPR56361.2022.9956494"},{"issue":"5","key":"2610_CR51","doi-asserted-by":"publisher","first-page":"2411","DOI":"10.1109\/TCSS.2022.3183046","volume":"10","author":"K Maity","year":"2023","unstructured":"Maity K, Saha S, Bhattacharyya P (2023) Emoji, sentiment and emotion aided cyberbullying detection in hinglish. IEEE Trans Comput Soc Syst 10(5):2411\u20132420","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"5","key":"2610_CR52","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/s12559-016-9389-5","volume":"8","author":"A Gepperth","year":"2016","unstructured":"Gepperth A, Karaoguz C (2016) A bio-inspired incremental learning architecture for applied perceptual problems. Cogn Comput 8(5):924\u2013934","journal-title":"Cogn Comput"},{"key":"2610_CR53","doi-asserted-by":"crossref","unstructured":"Li L, Zhang Y, Chen L (2023) Prompt distillation for efficient llm-based recommendation. In: Proceedings of the international conference on information and knowledge management CIKM, pp 1348\u20131357","DOI":"10.1145\/3583780.3615017"},{"key":"2610_CR54","unstructured":"Duh K, Fujino A, Nagata M (2011) Is machine translation ripe for cross-lingual sentiment classification? In: The annual meeting of the association for computational linguistics (ACL), pp 429\u2013433"},{"key":"2610_CR55","unstructured":"Chen M, Xu ZE, Weinberger KQ, Sha F (2012) Marginalized denoising autoencoders for domain adaptation. In: Proceedings of the international conference on machine learning ICML"},{"key":"2610_CR56","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1162\/tacl_a_00039","volume":"6","author":"X Chen","year":"2018","unstructured":"Chen X, Sun Y, Athiwaratkun B, Cardie C, Weinberger KQ (2018) Adversarial deep averaging networks for cross-lingual sentiment classification. Trans Assoc Comput Linguist 6:557\u2013570","journal-title":"Trans Assoc Comput Linguist"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02610-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02610-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02610-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:00:34Z","timestamp":1757156434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02610-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":56,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2610"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02610-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,15]]},"assertion":[{"value":"13 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 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":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}