{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:06:00Z","timestamp":1775228760391,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71772107"],"award-info":[{"award-number":["71772107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shandong Province of China","award":["ZR2023MF070"],"award-info":[{"award-number":["ZR2023MF070"]}]},{"name":"Shandong Education Quality Improvement Plan for Postgraduate"},{"name":"Open Research Fund of Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety","award":["CSBD2022-ZD01"],"award-info":[{"award-number":["CSBD2022-ZD01"]}]},{"name":"Shandong University of Science and Technology Research Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s13042-024-02354-6","type":"journal-article","created":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T08:02:47Z","timestamp":1726300967000},"page":"1589-1605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Propagation tree says: dynamic evolution characteristics learning approach for rumor detection"],"prefix":"10.1007","volume":"16","author":[{"given":"Shouhao","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Shujuan","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Jiandong","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Xianwen","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"2354_CR1","doi-asserted-by":"publisher","unstructured":"Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J (2020) Rumor detection on social media with bi-directional graph convolutional networks. Proceedings of the AAAI Conference on artificial intelligence 34(01):549\u2013556. https:\/\/doi.org\/10.1609\/aaai.v34i01.5393","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"2354_CR2","doi-asserted-by":"publisher","unstructured":"Cui C, Jia C (2024) Propagation tree is not deep: adaptive graph contrastive learning approach for rumor detection. Proceedings of the AAAI Conference on artificial intelligence 38(1):73\u201381. https:\/\/doi.org\/10.1609\/aaai.v38i1.27757","DOI":"10.1609\/aaai.v38i1.27757"},{"issue":"8","key":"2354_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0256039","volume":"16","author":"J Choi","year":"2021","unstructured":"Choi J, Ko T, Choi Y, Byun H, Kim C (2021) Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. PLoS One 16(8):1\u201317. https:\/\/doi.org\/10.1371\/journal.pone.0256039","journal-title":"PLoS One"},{"key":"2354_CR4","doi-asserted-by":"publisher","DOI":"10.1037\/11503-000","volume-title":"Rumor psychology: social and organizational approaches","author":"N DiFonzo","year":"2007","unstructured":"DiFonzo N, Bordia P (2007) Rumor psychology: social and organizational approaches. American Psychological Association, Washington D.C. https:\/\/doi.org\/10.1037\/11503-000"},{"key":"2354_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119291","volume":"213","author":"Y Guo","year":"2023","unstructured":"Guo Y, Ji S, Cao N, Chiu DKW, Su N, Zhang C (2023) Mdg: fusion learning of the maximal diffusion, deep propagation and global structure features of fake news. Expert Syst Appl 213:119291. https:\/\/doi.org\/10.1016\/j.eswa.2022.119291","journal-title":"Expert Syst Appl"},{"key":"2354_CR6","doi-asserted-by":"publisher","unstructured":"Gao T, Yao X, Chen D (2021) SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on empirical methods in natural language processing, pp. 6894\u20136910. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.552","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"2354_CR7","doi-asserted-by":"publisher","unstructured":"He Z, Li C, Zhou F, Yang Y (2021) Rumor detection on social media with event augmentations. In: Proceedings of the 44th International ACM SIGIR Conference on research and development in information retrieval. SIGIR \u201921, pp. 2020\u20132024. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3404835.3463001","DOI":"10.1145\/3404835.3463001"},{"key":"2354_CR8","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-60240-0_2","volume-title":"Social, cultural, and behavioral modeling","author":"Z Jin","year":"2017","unstructured":"Jin Z, Cao J, Guo H, Zhang Y, Wang Y, Luo J (2017) Detection and analysis of 2016 us presidential election related rumors on twitter. In: Lee D, Lin Y-R, Osgood N, Thomson R (eds) Social, cultural, and behavioral modeling. Springer, Cham, pp 14\u201324. https:\/\/doi.org\/10.1007\/978-3-319-60240-0_2"},{"key":"2354_CR9","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"issue":"1","key":"2354_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0168344","volume":"12","author":"S Kwon","year":"2017","unstructured":"Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PLoS One 12(1):1\u201319. https:\/\/doi.org\/10.1371\/journal.pone.0168344","journal-title":"PLoS One"},{"key":"2354_CR11","doi-asserted-by":"publisher","unstructured":"Khoo LMS, Chieu HL, Qian Z, Jiang J (2020) Interpretable rumor detection in microblogs by attending to user interactions. Proceedings of the AAAI Conference on artificial intelligence 34(05):8783\u20138790. https:\/\/doi.org\/10.1609\/aaai.v34i05.6405","DOI":"10.1609\/aaai.v34i05.6405"},{"key":"2354_CR12","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-1-4842-5364-9_2","volume-title":"Introduction to PyTorch","author":"N Ketkar","year":"2021","unstructured":"Ketkar N, Moolayil J (2021) Introduction to PyTorch. Apress, Berkeley, pp 27\u201391. https:\/\/doi.org\/10.1007\/978-1-4842-5364-9_2"},{"issue":"7","key":"2354_CR13","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1007\/s13042-021-01503-5","volume":"13","author":"AS Karnyoto","year":"2022","unstructured":"Karnyoto AS, Sun C, Liu B, Wang X (2022) Augmentation and heterogeneous graph neural network for aaai2021-covid-19 fake news detection. Int J Mach Learn Cybern 13(7):2033\u20132043. https:\/\/doi.org\/10.1007\/s13042-021-01503-5","journal-title":"Int J Mach Learn Cybern"},{"key":"2354_CR14","doi-asserted-by":"publisher","unstructured":"Lu Y-J, Li C-T (2020) GCAN: graph-aware co-attention networks for explainable fake news detection on social media. https:\/\/doi.org\/10.48550\/arXiv.2004.11648","DOI":"10.48550\/arXiv.2004.11648"},{"key":"2354_CR15","doi-asserted-by":"publisher","unstructured":"Liu Y, Liu P (2021) SimCLS: A simple framework for contrastive learning of abstractive summarization. In: Zong C, Xia F, Li W, Navigli R (eds) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on natural language processing (Volume 2: Short Papers), pp. 1065\u20131072. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-short.135","DOI":"10.18653\/v1\/2021.acl-short.135"},{"issue":"6","key":"2354_CR16","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1109\/TNNLS.2021.3114027","volume":"33","author":"C Li","year":"2022","unstructured":"Li C, Peng H, Li J, Sun L, Lyu L, Wang L, Yu PS, He L (2022) Joint stance and rumor detection in hierarchical heterogeneous graph. IEEE Trans Neural Netw Learn Syst 33(6):2530\u20132542. https:\/\/doi.org\/10.1109\/TNNLS.2021.3114027","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"2354_CR17","doi-asserted-by":"publisher","first-page":"4887","DOI":"10.1109\/TNNLS.2022.3161697","volume":"35","author":"B Liu","year":"2024","unstructured":"Liu B, Sun X, Meng Q, Yang X, Lee Y, Cao J, Luo J, Lee RK-W (2024) Nowhere to hide: online rumor detection based on retweeting graph neural networks. IEEE Trans Neural Netw Learn Syst 35(4):4887\u20134898. https:\/\/doi.org\/10.1109\/TNNLS.2022.3161697","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2354_CR18","doi-asserted-by":"publisher","unstructured":"Liu Y, Wu Y-F (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on artificial intelligence 32(1), https:\/\/doi.org\/10.1609\/aaai.v32i1.11268","DOI":"10.1609\/aaai.v32i1.11268"},{"issue":"4","key":"2354_CR19","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1109\/TCBB.2021.3069441","volume":"19","author":"D Li","year":"2022","unstructured":"Li D, Zhang S, Ma X (2022) Dynamic module detection in temporal attributed networks of cancers. IEEE\/ACM Trans Comput Biol Bioinf 19(4):2219\u20132230. https:\/\/doi.org\/10.1109\/TCBB.2021.3069441","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2354_CR20","unstructured":"Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the 25th International Joint Conference on artificial intelligence (IJCAI 2016), pp 3818\u20133824"},{"key":"2354_CR21","doi-asserted-by":"publisher","unstructured":"Ma J, Gao W, Wei Z, Lu Y, Wong K-F (2015) Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on information and knowledge management. CIKM \u201915, pp. 1751\u20131754. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2806416.2806607","DOI":"10.1145\/2806416.2806607"},{"key":"2354_CR22","doi-asserted-by":"publisher","unstructured":"Ma J, Gao W, Wong K-F (2017) Detect rumors in microblog posts using propagation structure via kernel learning. In: Barzilay R, Kan M-Y (eds) Proceedings of the 55th Annual Meeting of the Association for computational linguistics (Volume 1: Long Papers), pp. 708\u2013717. Association for Computational Linguistics, Vancouver, Canada. https:\/\/doi.org\/10.18653\/v1\/P17-1066","DOI":"10.18653\/v1\/P17-1066"},{"key":"2354_CR23","doi-asserted-by":"publisher","unstructured":"Ma J, Gao W, Wong K-F (2018) Rumor detection on Twitter with tree-structured recursive neural networks. In: Gurevych I, Miyao Y (eds) Proceedings of the 56th Annual Meeting of the Association for computational linguistics (Volume 1: Long Papers), pp. 1980\u20131989. Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-1184","DOI":"10.18653\/v1\/P18-1184"},{"key":"2354_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3319661","author":"J Ma","year":"2023","unstructured":"Ma J, Liu Y, Han M, Hu C, Ju Z (2023) Propagation structure fusion for rumor detection based on node-level contrastive learning. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3319661","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2354_CR25","doi-asserted-by":"publisher","unstructured":"Nguyen V-H, Sugiyama K, Nakov P, Kan M-Y (2020) Fang: leveraging social context for fake news detection using graph representation. In: Proceedings of the 29th ACM International Conference on information & knowledge management. CIKM \u201920, pp. 1165\u20131174. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3340531.3412046","DOI":"10.1145\/3340531.3412046"},{"issue":"3","key":"2354_CR26","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TC.2021.3057082","volume":"71","author":"H Peng","year":"2022","unstructured":"Peng H, Yang R, Wang Z, Li J, He L, Yu PS, Zomaya AY, Ranjan R (2022) Lime: low-cost and incremental learning for dynamic heterogeneous information networks. IEEE Trans Comput 71(3):628\u2013642. https:\/\/doi.org\/10.1109\/TC.2021.3057082","journal-title":"IEEE Trans Comput"},{"key":"2354_CR27","doi-asserted-by":"publisher","DOI":"10.1145\/3660522","author":"H Peng","year":"2024","unstructured":"Peng H, Zhang J, Huang X, Hao Z, Li A, Yu Z, Yu PS (2024) Unsupervised social bot detection via structural information theory. ACM Trans Inf Syst. https:\/\/doi.org\/10.1145\/3660522","journal-title":"ACM Trans Inf Syst"},{"issue":"5","key":"2354_CR28","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","volume":"24","author":"G Salton","year":"1988","unstructured":"Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inform Process Manag 24(5):513\u2013523. https:\/\/doi.org\/10.1016\/0306-4573(88)90021-0","journal-title":"Inform Process Manag"},{"key":"2354_CR29","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.neucom.2022.07.057","volume":"505","author":"C Song","year":"2022","unstructured":"Song C, Teng Y, Zhu Y, Wei S, Wu B (2022) Dynamic graph neural network for fake news detection. Neurocomputing 505:362\u2013374. https:\/\/doi.org\/10.1016\/j.neucom.2022.07.057","journal-title":"Neurocomputing"},{"issue":"9","key":"2354_CR30","doi-asserted-by":"publisher","first-page":"9128","DOI":"10.1109\/TKDE.2022.3221438","volume":"35","author":"X Sun","year":"2023","unstructured":"Sun X, Yin H, Liu B, Meng Q, Cao J, Zhou A, Chen H (2023) Structure learning via meta-hyperedge for dynamic rumor detection. IEEE Trans Knowl Data Eng 35(9):9128\u20139139. https:\/\/doi.org\/10.1109\/TKDE.2022.3221438","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2354_CR31","doi-asserted-by":"publisher","unstructured":"Sun M, Zhang X, Zheng J, Ma G (2022) Ddgcn: dual dynamic graph convolutional networks for rumor detection on social media. Proceedings of the AAAI Conference on artificial intelligence 36(4):4611\u20134619. https:\/\/doi.org\/10.1609\/aaai.v36i4.20385","DOI":"10.1609\/aaai.v36i4.20385"},{"key":"2354_CR32","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"2354_CR33","doi-asserted-by":"publisher","unstructured":"Wang S, Kong Q, Wang Y, Wang L (2019) Enhancing rumor detection in social media using dynamic propagation structures. In: 2019 IEEE International Conference on intelligence and security informatics (ISI), pp 41\u201346. https:\/\/doi.org\/10.1109\/ISI.2019.sps10","DOI":"10.1109\/ISI.2019.sps10"},{"issue":"11","key":"2354_CR34","doi-asserted-by":"publisher","first-page":"3993","DOI":"10.1007\/s13042-023-01877-8","volume":"14","author":"S Wan","year":"2023","unstructured":"Wan S, Tang B, Dong F, Wang M, Yang G (2023) A writing style-based multi-task model with the hierarchical attention for rumor detection. Int J Mach Learn Cybern 14(11):3993\u20134008. https:\/\/doi.org\/10.1007\/s13042-023-01877-8","journal-title":"Int J Mach Learn Cybern"},{"key":"2354_CR35","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3389\/frma.2022.1055348","volume":"7","author":"S Wei","year":"2023","unstructured":"Wei S, Wu B, Xiang A, Zhu Y, Song C (2023) Dgtr: dynamic graph transformer for rumor detection. Front Res Metrics Anal 7:10. https:\/\/doi.org\/10.3389\/frma.2022.1055348","journal-title":"Front Res Metrics Anal"},{"key":"2354_CR36","doi-asserted-by":"publisher","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks?. https:\/\/doi.org\/10.48550\/arXiv.1810.00826","DOI":"10.48550\/arXiv.1810.00826"},{"key":"2354_CR37","doi-asserted-by":"crossref","unstructured":"Yang X, Lyu Y, Tian T, Liu Y, Liu Y, Zhang X (2021) Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the Twenty-Ninth International Joint Conference on artificial intelligence. IJCAI\u201920","DOI":"10.24963\/ijcai.2020\/197"},{"issue":"5","key":"2354_CR38","doi-asserted-by":"publisher","first-page":"5201","DOI":"10.1007\/s11227-022-04831-7","volume":"79","author":"P Yang","year":"2023","unstructured":"Yang P, Leng J, Zhao G, Li W, Fang H (2023) Rumor detection driven by graph attention capsule network on dynamic propagation structures. J Supercomput 79(5):5201\u20135222. https:\/\/doi.org\/10.1007\/s11227-022-04831-7","journal-title":"J Supercomput"},{"key":"2354_CR39","doi-asserted-by":"publisher","unstructured":"Yuan C, Ma Q, Zhou W, Han J, Hu S (2019) Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In: 2019 IEEE International Conference on data mining (ICDM), pp. 796\u2013805. https:\/\/doi.org\/10.1109\/ICDM.2019.00090","DOI":"10.1109\/ICDM.2019.00090"},{"key":"2354_CR40","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-319-67217-5_8","volume-title":"Social informatics","author":"A Zubiaga","year":"2017","unstructured":"Zubiaga A, Liakata M, Procter R (2017) Exploiting context for rumour detection in social media. In: Ciampaglia GL, Mashhadi A, Yasseri T (eds) Social informatics. Springer, Cham, pp 109\u2013123. https:\/\/doi.org\/10.1007\/978-3-319-67217-5_8"},{"key":"2354_CR41","doi-asserted-by":"publisher","unstructured":"Zhang D, Nan F, Wei X, Li S-W, Zhu H, McKeown K, Nallapati R, Arnold AO, Xiang B (2021) Supporting clustering with contrastive learning. In: Toutanova K, Rumshisky A, Zettlemoyer L, Hakkani-Tur D, Beltagy I, Bethard S, Cotterell R, Chakraborty T, Zhou Y (eds) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5419\u20135430. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.427","DOI":"10.18653\/v1\/2021.naacl-main.427"},{"key":"2354_CR42","doi-asserted-by":"publisher","unstructured":"Zhao Z, Resnick P, Mei Q (2015) Enquiring minds: Early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web. WWW \u201915, pp. 1395\u20131405. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https:\/\/doi.org\/10.1145\/2736277.2741637","DOI":"10.1145\/2736277.2741637"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02354-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02354-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02354-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T07:37:50Z","timestamp":1739950670000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02354-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,14]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2354"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02354-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,14]]},"assertion":[{"value":"25 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors read and approved the final version of the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}