{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:07:27Z","timestamp":1778947647985,"version":"3.51.4"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61836016"],"award-info":[{"award-number":["61836016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Project of Guangxi Science and Technology","award":["GuiKeAB23026040"],"award-info":[{"award-number":["GuiKeAB23026040"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71774159"],"award-info":[{"award-number":["71774159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2021T140707"],"award-info":[{"award-number":["2021T140707"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangsu Postdoctoral Science Foundation","award":["2021K565C"],"award-info":[{"award-number":["2021K565C"]}]},{"name":"Science and Technology Foundation of Xuzhou","award":["KC22047"],"award-info":[{"award-number":["KC22047"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            It has become a cardinal task to identify fake information (misinformation) on social media, because it has significantly harmed the government and the public. There are many spam bots maliciously retweeting misinformation. This study proposes an efficient model for detecting misinformation with self-supervised contrastive learning. A\n            <jats:bold>B<\/jats:bold>\n            ayesian graph\n            <jats:bold>L<\/jats:bold>\n            ocal extrema\n            <jats:bold>C<\/jats:bold>\n            onvolution (BLC) is first proposed to aggregate node features in the graph structure. The BLC approach considers unreliable relationships and uncertainties in the propagation structure, and the differences between nodes and neighboring nodes are emphasized in the attributes. Then, a new long-tail strategy for matching long-tail users with the global social network is advocated to avoid over-concentration on high-degree nodes in graph neural networks. Finally, the proposed model is experimentally evaluated with two public Twitter datasets and demonstrates that the proposed long-tail strategy significantly improves the effectiveness of existing graph-based methods in terms of detecting misinformation. The robustness of BLC has also been examined on three graph datasets and demonstrates that it consistently outperforms traditional algorithms when perturbed by 15% of a dataset.\n          <\/jats:p>","DOI":"10.1145\/3639408","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T21:33:51Z","timestamp":1704317631000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Bayesian Graph Local Extrema Convolution with Long-tail Strategy for Misinformation Detection"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7632-8411","authenticated-orcid":false,"given":"Guixian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Engineering Research Center of Mine Digitalization, Artificial Intelligence Research Institute, China University of Mining and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9981-2970","authenticated-orcid":false,"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining &amp; Security, College of Computer Science &amp; Engineering, Guangxi Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-9817","authenticated-orcid":false,"given":"Guan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Engineering Research Center of Mine Digitalization, Artificial Intelligence Research Institute, China University of Mining and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18113"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3316504"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0956797615594620"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1017\/XPS.2021.18"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"e_1_3_1_7_2","first-page":"1613","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural network. In Proceedings of the International Conference on Machine Learning. PMLR, 1613\u20131622."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963500"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1137\/070710111"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.29"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872518.2889302"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481949"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482019"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487351.3488336"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1177\/107769900007700304"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-020-00994-6"},{"key":"e_1_3_1_17_2","article-title":"Practical variational inference for neural networks","volume":"24","author":"Graves Alex","year":"2011","unstructured":"Alex Graves. 2011. Practical variational inference for neural networks. Adv. Neural Inf. Process. Syst. 24 (2011).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau2706"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271709"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2996197"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463001"},{"key":"e_1_3_1_23_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hou Yifan","year":"2020","unstructured":"Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, and Ming-Chang Yang. 2020. Measuring and improving the use of graph information in graph neural networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10382"},{"key":"e_1_3_1_26_2","article-title":"Auto-encoding variational Bayes","author":"Kingma Diederik P.","year":"2013","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).","journal-title":"arXiv preprint arXiv:1312.6114"},{"key":"e_1_3_1_27_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations. OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1002\/pra2.2018.14505501028"},{"issue":"3","key":"e_1_3_1_29_2","article-title":"Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter","volume":"12","author":"Kouzy Ramez","year":"2020","unstructured":"Ramez Kouzy, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter. Cureus 12, 3 (2020).","journal-title":"Cureus"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-12385-7_58"},{"key":"e_1_3_1_31_2","first-page":"705","volume-title":"Findings of the Association for Computational Linguistics (ACL-IJCNLP\u201921","author":"Li Jiawen","year":"2021","unstructured":"Jiawen Li, Shiwen Ni, and Hung-Yu Kao. 2021. Meet the truth: Leverage objective facts and subjective views for interpretable rumor detection. In Findings of the Association for Computational Linguistics (ACL-IJCNLP\u201921). 705\u2013715."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_32"},{"key":"e_1_3_1_33_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019).","journal-title":"arXiv preprint arXiv:1907.11692"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467276"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3391250"},{"key":"e_1_3_1_36_2","first-page":"3818","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI\u201916)","author":"Ma Jing","year":"2016","unstructured":"Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI\u201916). International Joint Conferences on Artificial Intelligence Organization (IJCAI), 3818\u20133824."},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806607"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/0005-2795(75)90109-9"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/1964858.1964869"},{"key":"e_1_3_1_40_2","first-page":"3111","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 3111\u20133119."},{"issue":"2","key":"e_1_3_1_41_2","first-page":"e7157","article-title":"What are people tweeting about Zika? An exploratory study concerning its symptoms, treatment, transmission, and prevention","volume":"3","author":"Miller Michele","year":"2017","unstructured":"Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William Romine, and Amit Sheth. 2017. What are people tweeting about Zika? An exploratory study concerning its symptoms, treatment, transmission, and prevention. JMIR Pub. Health Surveill. 3, 2 (2017), e7157.","journal-title":"JMIR Pub. Health Surveill."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.11.016"},{"key":"e_1_3_1_43_2","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-GAN: Training generative neural samplers using variational divergence minimization. In Proceedings of the 30th International Conference on Neural Information Processing Systems."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494563"},{"key":"e_1_3_1_45_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards deep graph convolutional networks on node classification. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_1_47_2","first-page":"424","volume-title":"Proceedings of the International Conference on Complex Networks and their Applications","author":"Schuchard Ross","year":"2018","unstructured":"Ross Schuchard, Andrew Crooks, Anthony Stefanidis, and Arie Croitoru. 2018. Bots in nets: Empirical comparative analysis of bot evidence in social networks. In Proceedings of the International Conference on Complex Networks and their Applications. Springer, 424\u2013436."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3137597.3137600"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1803470115"},{"key":"e_1_3_1_51_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun, Jordon Hoffman, Vikas Verma, and Jian Tang. 2020. InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_52_2","volume-title":"Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201914)","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201914)."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411872"},{"key":"e_1_3_1_54_2","first-page":"1792","volume-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics","author":"Vashishth Shikhar","year":"2019","unstructured":"Shikhar Vashishth, Prateek Yadav, Manik Bhandari, and Partha Talukdar. 2019. Confidence-based graph convolutional networks for semi-supervised learning. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1792\u20131801."},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3299886"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00063"},{"key":"e_1_3_1_59_2","first-page":"6861","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of the International Conference on Machine Learning. PMLR, 6861\u20136871."},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2015.7113322"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102902"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/500"},{"key":"e_1_3_1_63_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_64_2","first-page":"1417","volume-title":"Proceedings of the 29th International Joint Conferences on Artificial Intelligence","author":"Yang Xiaoyu","year":"2021","unstructured":"Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, and Xi Zhang. 2021. Rumor detection on social media with graph structured adversarial learning. In Proceedings of the 29th International Joint Conferences on Artificial Intelligence. 1417\u20131423."},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496212"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00090"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2014.03.021"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICKG52313.2021.00018"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892019"},{"key":"e_1_3_1_70_2","doi-asserted-by":"crossref","unstructured":"Haiqi Zhang Guangquan Lu Mengmeng Zhan and Beixian Zhang. 2022. Semi-supervised classification of graph convolutional networks with laplacian rank constraints. Neural Processing Letters 54 4 (2022) 2645\u20132656.","DOI":"10.1007\/s11063-020-10404-7"},{"issue":"3","key":"e_1_3_1_71_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3442204","article-title":"Probability ordinal-preserving semantic hashing for large-scale image retrieval","volume":"15","author":"Zhang Zheng","year":"2021","unstructured":"Zheng Zhang, Xiaofeng Zhu, Guangming Lu, and Yudong Zhang. 2021. Probability ordinal-preserving semantic hashing for large-scale image retrieval. ACM Trans. Knowl. Discov. Data 15, 3 (2021), 1\u201322.","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741637"},{"key":"e_1_3_1_73_2","first-page":"11458","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zheng Cheng","year":"2020","unstructured":"Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. 2020. Robust graph representation learning via neural sparsification. In Proceedings of the International Conference on Machine Learning. PMLR, 11458\u201311468."},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_1_76_2","first-page":"2438","volume-title":"Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING\u201916)","author":"Zubiaga Arkaitz","year":"2016","unstructured":"Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, and Michal Lukasik. 2016. Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING\u201916). 2438\u20132448."},{"key":"e_1_3_1_77_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial attacks on graph neural networks via meta learning. In Proceedings of the International Conference on Learning Representations."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639408","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639408","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:54:11Z","timestamp":1750287251000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":76,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3639408"],"URL":"https:\/\/doi.org\/10.1145\/3639408","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,12]]},"assertion":[{"value":"2022-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}