{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:13:10Z","timestamp":1740111190699,"version":"3.37.3"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,7,9]],"date-time":"2023-07-09T00:00:00Z","timestamp":1688860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001799","name":"Murdoch University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001799","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.compbiomed.2023.107232","type":"journal-article","created":{"date-parts":[[2023,7,9]],"date-time":"2023-07-09T08:48:53Z","timestamp":1688892533000},"page":"107232","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media"],"prefix":"10.1016","volume":"164","author":[{"given":"Kok Kiang","family":"Long","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen Wai Hang","family":"Kwok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayne","family":"Kotz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-5258-0532","authenticated-orcid":false,"given":"Guanjin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"author":"World Health Organization","key":"10.1016\/j.compbiomed.2023.107232_bib1"},{"issue":"8","key":"10.1016\/j.compbiomed.2023.107232_bib2","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1093\/jamia\/ocaa116","article-title":"Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource","volume":"27","author":"Sarker","year":"2020","journal-title":"J. Am. Med. Inf. Assoc."},{"issue":"2","key":"10.1016\/j.compbiomed.2023.107232_bib3","doi-asserted-by":"crossref","DOI":"10.2196\/19509","article-title":"Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on twitter: retrospective big data infoveillance study","volume":"6","author":"Mackey","year":"2020","journal-title":"JMIR Publ. Health Surveil."},{"issue":"1","key":"10.1016\/j.compbiomed.2023.107232_bib4","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-98396-9","article-title":"Evaluation of twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK","volume":"11","author":"Cheng","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2023.107232_bib5","article-title":"A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK","volume":"8","author":"Golder","year":"2022","journal-title":"Dig. Health"},{"issue":"1","key":"10.1016\/j.compbiomed.2023.107232_bib6","doi-asserted-by":"crossref","DOI":"10.1186\/s12889-021-10827-4","article-title":"A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States","volume":"21","author":"Cuomo","year":"2021","journal-title":"BMC Publ. Health"},{"year":"2020","series-title":"WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets","author":"Nguyen","key":"10.1016\/j.compbiomed.2023.107232_bib7"},{"issue":"5","key":"10.1016\/j.compbiomed.2023.107232_bib8","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1007\/s10489-020-02029-z","article-title":"Design and analysis of a large-scale COVID-19 tweets dataset","volume":"51","author":"Lamsal","year":"2021","journal-title":"Appl. Intell."},{"key":"10.1016\/j.compbiomed.2023.107232_bib9","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: recent progress and new challenges","volume":"38","author":"Jing","year":"2017","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.compbiomed.2023.107232_bib10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s13042-010-0001-0","article-title":"Understanding bag-of-words model: a statistical framework","volume":"1","author":"Zhang","year":"2010","journal-title":"Int.J. Mach.Learn. Cybernet."},{"key":"10.1016\/j.compbiomed.2023.107232_bib11","series-title":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","first-page":"61","article-title":"Document clustering: TF-IDF approach","author":"Bafna","year":"2016"},{"issue":"1","key":"10.1016\/j.compbiomed.2023.107232_bib12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1017\/S1351324916000334","volume":"23","author":"Church","year":"2017","journal-title":"Word2Vec, Nat. Lang.Eng."},{"year":"2014","series-title":"Distributed Representations of Sentences and Documents","author":"Le","key":"10.1016\/j.compbiomed.2023.107232_bib13"},{"key":"10.1016\/j.compbiomed.2023.107232_bib14","series-title":"Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding","first-page":"4171","author":"Kenton","year":"2019"},{"key":"10.1016\/j.compbiomed.2023.107232_bib15","article-title":"Two view learning: SVM-2K, theory and practice","volume":"18","author":"Farquhar","year":"2005","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2023.107232_bib16","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","article-title":"An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics","volume":"250","author":"L\u00f3pez","year":"2013","journal-title":"Inf. Sci."},{"issue":"2","key":"10.1016\/j.compbiomed.2023.107232_bib17","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"issue":"2","key":"10.1016\/j.compbiomed.2023.107232_bib18","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Network."},{"issue":"3","key":"10.1016\/j.compbiomed.2023.107232_bib19","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/biomet\/58.3.433","article-title":"Canonical analysis of several sets of variables","volume":"58","author":"Kettenring","year":"1971","journal-title":"Biometrika"},{"issue":"Jul","key":"10.1016\/j.compbiomed.2023.107232_bib20","first-page":"1","article-title":"Kernel independent component analysis","volume":"3","author":"Bach","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compbiomed.2023.107232_bib21","series-title":"Proceedings of the 8th International ISCRAM Conference","article-title":"Classifying text messages for the Haiti earthquake","author":"Caragea","year":"2011"},{"key":"10.1016\/j.compbiomed.2023.107232_bib22","article-title":"Automatic classification of disaster-related tweets","volume":"vol. 62","author":"Parilla-Ferrer","year":"2014"},{"key":"10.1016\/j.compbiomed.2023.107232_bib23","first-page":"385","article-title":"Natural language processing to the rescue? extracting \u201dsituational awareness\u201d tweets during mass emergency","volume":"vol. 5","author":"Verma","year":"2011"},{"key":"10.1016\/j.compbiomed.2023.107232_bib24","series-title":"2019 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Tweet act classification: a deep learning based classifier for recognizing speech acts in twitter","author":"Saha","year":"2019"},{"issue":"11","key":"10.1016\/j.compbiomed.2023.107232_bib25","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3390\/fi10110113","article-title":"Chinese text classification model based on deep learning","volume":"10","author":"Li","year":"2018","journal-title":"Future Internet"},{"issue":"1","key":"10.1016\/j.compbiomed.2023.107232_bib26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TFUZZ.2016.2637405","article-title":"Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system","volume":"25","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.compbiomed.2023.107232_bib27","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: recent progress and new challenges","volume":"38","author":"Zhao","year":"2017","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compbiomed.2023.107232_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107982","article-title":"Easy domain adaptation for cross-subject multi-view emotion recognition","volume":"239","author":"Chen","year":"2022","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.compbiomed.2023.107232_bib29","series-title":"Twenty-third International Joint Conference on Artificial Intelligence","article-title":"Multi-view maximum entropy discrimination","author":"Sun","year":"2013"},{"key":"10.1016\/j.compbiomed.2023.107232_bib30","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.ins.2016.06.004","article-title":"Consensus and complementarity based maximum entropy discrimination for multi-view classification","volume":"367","author":"Chao","year":"2016","journal-title":"Inf. Sci."},{"issue":"2","key":"10.1016\/j.compbiomed.2023.107232_bib31","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s11063-018-9935-0","article-title":"Multi-view opinion mining with deep learning","volume":"50","author":"Huang","year":"2019","journal-title":"Neural Process. Lett."},{"key":"10.1016\/j.compbiomed.2023.107232_bib32","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2577","article-title":"AE2-nets: autoencoder in autoencoder networks","author":"Zhang","year":"2019"},{"issue":"11","key":"10.1016\/j.compbiomed.2023.107232_bib33","doi-asserted-by":"crossref","first-page":"12414","DOI":"10.1109\/TCYB.2021.3084464","article-title":"Multiview graph restricted Boltzmann machines","volume":"52","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"8","key":"10.1016\/j.compbiomed.2023.107232_bib34","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1007\/s13042-020-01081-y","article-title":"Least squares support vector machines with fast leave-one-out AUC optimization on imbalanced prostate cancer data","volume":"11","author":"Wang","year":"2020","journal-title":"Int.J. Mach.Learn. Cybernet."},{"key":"10.1016\/j.compbiomed.2023.107232_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104527","article-title":"A multiple combined method for rebalancing medical data with class imbalances","volume":"134","author":"Wang","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2023.107232_bib36","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"10.1016\/j.compbiomed.2023.107232_bib37","doi-asserted-by":"crossref","first-page":"7919","DOI":"10.1109\/TSMC.2020.2982226","article-title":"AUC-based extreme learning machines for supervised and semi-supervised imbalanced classification","volume":"51","author":"Wang","year":"2020","journal-title":"IEEE Transact. Syst. Man Cybernet.: Systems"},{"key":"10.1016\/j.compbiomed.2023.107232_bib38","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary","volume":"61","author":"Fern\u00e1ndez","year":"2018","journal-title":"J. Artif. Intell. Res."},{"issue":"9","key":"10.1016\/j.compbiomed.2023.107232_bib39","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1109\/TNNLS.2017.2751612","article-title":"Classification of imbalanced data by oversampling in kernel space of support vector machines","volume":"29","author":"Mathew","year":"2017","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"issue":"5","key":"10.1016\/j.compbiomed.2023.107232_bib40","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1109\/TCYB.2020.3008963","article-title":"Deep cross-output knowledge transfer using stacked-structure least-squares support vector machines","volume":"52","author":"Wang","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.compbiomed.2023.107232_bib41","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.procs.2019.09.229","article-title":"Dealing with data imbalance in text classification","volume":"159","author":"Padurariu","year":"2019","journal-title":"Proc. Comput. Sci."},{"key":"10.1016\/j.compbiomed.2023.107232_bib42","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1109\/TMM.2020.3035277","article-title":"Image-text multimodal emotion classification via multi-view attentional network","volume":"23","author":"Yang","year":"2020","journal-title":"IEEE Trans. Multimed."},{"issue":"11","key":"10.1016\/j.compbiomed.2023.107232_bib43","doi-asserted-by":"crossref","first-page":"3315","DOI":"10.1109\/JBHI.2020.2983365","article-title":"A multi-view deep neural network model for chemical-disease relation extraction from imbalanced datasets","volume":"24","author":"Mitra","year":"2020","journal-title":"IEEE J.Biomed. Health Inf."},{"key":"10.1016\/j.compbiomed.2023.107232_bib44","article-title":"A novel auc maximization imbalanced learning approach for predicting composite outcomes in covid-19 hospitalized patients","author":"Wang","year":"2023","journal-title":"IEEE J.Biomed. Health Inf."},{"issue":"3","key":"10.1016\/j.compbiomed.2023.107232_bib45","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compbiomed.2023.107232_bib46","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compbiomed.2015.05.015","article-title":"Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques","volume":"63","author":"Wang","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2023.107232_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105206","article-title":"Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis","volume":"143","author":"Xia","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"10.1016\/j.compbiomed.2023.107232_bib48","doi-asserted-by":"crossref","first-page":"6015","DOI":"10.1109\/TSMC.2019.2958647","article-title":"Formulating ensemble learning of SVMs into a single SVM formulation by negative agreement learning","volume":"51","author":"Zhou","year":"2020","journal-title":"IEEE Transact. Syst. Man Cybernet.: Systems"},{"issue":"12","key":"10.1016\/j.compbiomed.2023.107232_bib49","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: an overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput."},{"issue":"8","key":"10.1016\/j.compbiomed.2023.107232_bib50","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1109\/TNNLS.2017.2728139","article-title":"Multiview privileged support vector machines","volume":"29","author":"Tang","year":"2017","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"issue":"9","key":"10.1016\/j.compbiomed.2023.107232_bib51","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1109\/TNNLS.2018.2886341","article-title":"Less is more: a comprehensive framework for the number of components of ensemble classifiers","volume":"30","author":"Bonab","year":"2019","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"key":"10.1016\/j.compbiomed.2023.107232_bib52","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.ins.2016.06.004","article-title":"Consensus and complementarity based maximum entropy discrimination for multi-view classification","volume":"367\u2013368","author":"Chao","year":"2016","journal-title":"Inf. Sci."},{"key":"10.1016\/j.compbiomed.2023.107232_bib53","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.inffus.2022.08.014","article-title":"Comprehensive multi-view representation learning","volume":"89","author":"Zheng","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compbiomed.2023.107232_bib54","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.patcog.2018.11.015","article-title":"Adaptive-weighting discriminative regression for multi-view classification","volume":"88","author":"Yang","year":"2019","journal-title":"Pattern Recogn."},{"issue":"7","key":"10.1016\/j.compbiomed.2023.107232_bib55","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the roc curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.compbiomed.2023.107232_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114864","article-title":"Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts","volume":"177","author":"Yang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2023.107232_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115079","article-title":"Run beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method","volume":"181","author":"Ahmadianfar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2023.107232_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.advengsoft.2022.103276","article-title":"A new movement strategy of grey wolf optimizer for optimization problems and structural damage identification","volume":"173","author":"Sang-To","year":"2022","journal-title":"Adv. Eng. Software"},{"issue":"1","key":"10.1016\/j.compbiomed.2023.107232_bib59","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-03097-y","article-title":"Forecasting of excavation problems for high-rise building in vietnam using planet optimization algorithm","volume":"11","author":"Sang-To","year":"2021","journal-title":"Sci. Rep."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0010482523006972?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0010482523006972?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:55:01Z","timestamp":1736384101000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482523006972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":59,"alternative-id":["S0010482523006972"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compbiomed.2023.107232","relation":{},"ISSN":["0010-4825"],"issn-type":[{"type":"print","value":"0010-4825"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compbiomed.2023.107232","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"107232"}}