1932

Abstract

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)—the branch of artificial intelligence that interprets human language—can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.

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2021-07-20
2024-10-10
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Literature Cited

  1. 1. 
    WHO (World Health Organ.) 2020. Pneumonia of unknown cause—China Dis. Outbreak News, WHO Geneva: https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/
    [Google Scholar]
  2. 2. 
    WHO (World Health Organ.) 2020. Novel coronavirus (2019-nCoV): situation report, 22 Tech. Rep., WHO Geneva: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200211-sitrep-22-ncov.pdf
    [Google Scholar]
  3. 3. 
    WHO (World Health Organ.) 2020. Novel coronavirus (2019-nCoV): situation report, 10 Tech. Rep., WHO Geneva: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200130-sitrep-10-ncov.pdf
    [Google Scholar]
  4. 4. 
    Stegmann J. 2020. MeSH descriptors indicate the knowledge growth in the SARS-CoV-2/COVID-19 pandemic. arXiv:2005.06259 [cs.DL]
    [Google Scholar]
  5. 5. 
    Nowakowska J, Sobocińska J, Lewicki M, Lemańska Ż, Rzymski P. 2020. When science goes viral: the research response during three months of the COVID-19 outbreak. Biomed. Pharmacother. 129:110451
    [Google Scholar]
  6. 6. 
    Brainard J. 2020. New tools aim to tame pandemic paper tsunami. Science 368:924–25
    [Google Scholar]
  7. 7. 
    Valika TS, Maurrasse SE, Reichert L. 2020. A second pandemic? Perspective on information overload in the COVID-19 era. Otolaryngol. Head Neck Surg. 163:5931–33
    [Google Scholar]
  8. 8. 
    Soltani P, Patini R. 2020. Retracted COVID-19 articles: a side-effect of the hot race to publication. Scientometrics 125:819–22
    [Google Scholar]
  9. 9. 
    Yeo-Teh NSL, Tang BL 2020. An alarming retraction rate for scientific publications on coronavirus disease 2019 (COVID-19). Account. Res. 28:147–53
    [Google Scholar]
  10. 10. 
    Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S et al. 2020. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 7:547–60
    [Google Scholar]
  11. 11. 
    Tasnim S, Hossain MM, Mazumder H. 2020. Impact of rumors and misinformation on COVID-19 in social media. J. Prev. Med. Public Health 53:171–74
    [Google Scholar]
  12. 12. 
    Rathore FA, Farooq F. 2020. Information overload and infodemic in the COVID-19 pandemic. J. Pak. Med. Assoc. 70:S162–65
    [Google Scholar]
  13. 13. 
    Chen Q, Allot A, Lu Z. 2020. Keep up with the latest coronavirus research. Nature 579:193
    [Google Scholar]
  14. 14. 
    Xu J, Kim S, Song M, Jeong M, Kim D et al. 2020. Building a PubMed knowledge graph. Sci Data 7:1205
    [Google Scholar]
  15. 15. 
    Jensen LJ, Saric J, Bork P. 2006. Literature mining for the biologist: from information retrieval to biological discovery. Nat. Rev. Genet. 7:119–29
    [Google Scholar]
  16. 16. 
    Rzhetsky A, Seringhaus M, Gerstein M. 2008. Seeking a new biology through text mining. Cell 134:9–13
    [Google Scholar]
  17. 17. 
    Altman RB, Bergman CM, Blake J, Blaschke C, Cohen A et al. 2008. Text mining for biology-the way forward: opinions from leading scientists. Genome Biol 9:S7
    [Google Scholar]
  18. 18. 
    Fiorini N, Canese K, Starchenko G, Kireev E, Kim W et al. 2018. Best match: new relevance search for PubMed. PLOS Biol 16:e2005343
    [Google Scholar]
  19. 19. 
    Markowetz F. 2017. All biology is computational biology. PLOS Biol 15:e2002050
    [Google Scholar]
  20. 20. 
    Zhao S, Su C, Lu Z, Wang F 2020. Recent advances in biomedical literature mining. Brief. Bioinform. 2020:bbaa057
    [Google Scholar]
  21. 21. 
    Lever J, Zhao EY, Grewal J, Jones MR, Jones SJM. 2019. CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nat. Methods 16:505–7
    [Google Scholar]
  22. 22. 
    Huang L-C, Ross KE, Baffi TR, Drabkin H, Kochut KJ et al. 2018. Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources. Sci. Rep. 8:6518
    [Google Scholar]
  23. 23. 
    Sarker A, Ginn R, Nikfarjam A, O'Connor K, Smith K et al. 2015. Utilizing social media data for pharmacovigilance: a review. J. Biomed. Inform. 54:202–12
    [Google Scholar]
  24. 24. 
    Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. 2020. Challenges and opportunities for public health made possible by advances in natural language processing. Can. Commun. Dis. Rep. 46:161–69
    [Google Scholar]
  25. 25. 
    Westergaard D, Stærfeldt H-H, Tønsberg C, Jensen LJ, SJPcb Brunak 2018. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts. PLOS Comput. Biol. 14:e1005962
    [Google Scholar]
  26. 26. 
    Rebholz-Schuhmann D, Oellrich A, Hoehndorf R. 2012. Text-mining solutions for biomedical research: enabling integrative biology. Nat. Rev. Genet. 13:829–39
    [Google Scholar]
  27. 27. 
    WHO (World Health Organ.) 2020. COVID-19 coding in ICD-10. Slideshow, WHO, Geneva. https://www.who.int/classifications/icd/COVID-19-coding-icd10.pdf
    [Google Scholar]
  28. 28. 
    NLM (U.S. Natl. Lib. Med.) 2020. New MeSH supplementary concept record for coronavirus disease 2019 (COVID-19). NLM Technical Bulletin Feb. 14. https://www.nlm.nih.gov/pubs/techbull/jf20/brief/jf20_mesh_novel_coronavirus_disease.html
    [Google Scholar]
  29. 29. 
    Jiang S, Shi Z, Shu Y, Song J, Gao GF et al. 2020. A distinct name is needed for the new coronavirus. Lancet 395:949
    [Google Scholar]
  30. 30. 
    Leaman R, Lu Z 2020. A comprehensive dictionary and variability analysis of terms for COVID-19 and SARS-CoV-2. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020 K Verspoor, KB Cohen, M Conway, B de Bruijn, M Dredze et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  31. 31. 
    Odone A, Galea S, Stuckler D, Signorelli CUniv. Vita-Salute San Raffaele COVID-19 Lit. Monit. Work. Group 2020. The first 10,000 COVID-19 papers in perspective: Are we publishing what we should be publishing?. Eur. J. Public Health 30:5849–50
    [Google Scholar]
  32. 32. 
    Gazendam A, Ekhtiari S, Wong E, Madden K, Naji L et al. 2020. The “Infodemic” of journal publication associated with the novel coronavirus disease. J. Bone Joint Surg. Am. 102:e64
    [Google Scholar]
  33. 33. 
    Rabby MII. 2020. Current drugs with potential for treatment of COVID-19: a literature review. J. Pharm. Pharm. Sci. 23:58–64
    [Google Scholar]
  34. 34. 
    Siordia JA Jr. 2020. Epidemiology and clinical features of COVID-19: a review of current literature. J. Clin. Virol. 127:104357
    [Google Scholar]
  35. 35. 
    Srivastava S, Verma S, Kamthania M, Kaur R, Badyal RK et al. 2020. Structural basis for designing multiepitope vaccines against COVID-19 infection: in silico vaccine design and validation. JMIR Bioinform. Biotech. 1:e19371
    [Google Scholar]
  36. 36. 
    Keeling MJ, Hollingsworth TD, Read JM. 2020. Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19). J. Epidemiol. Community Health 74:861–66
    [Google Scholar]
  37. 37. 
    Wang LL, Lo K, Chandrasekhar Y, Reas R, Yang J et al. 2020. CORD-19: The COVID-19 Open Research Dataset. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 K Verspoor, KB Cohen, M Dredze, E Ferrera, J May et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  38. 38. 
    Trewartha A, Dagdelen J, Huo H, Cruse K, Wang Z et al. 2020. COVIDScholar: an automated COVID-19 research aggregation and analysis platform. arXiv:2012.03891 [cs.DL]
    [Google Scholar]
  39. 39. 
    Zhang E, Gupta N, Nogueira R, Cho K, Lin J. 2020. Rapidly deploying a neural search engine for the COVID-19 open research dataset: preliminary thoughts and lessons learned. arXiv:2004.05125 [cs.CL]
    [Google Scholar]
  40. 40. 
    Ludwig 2020. Welcome to LIA: Ludwig initiative against COVID-19. https://covid19.ludwig.guru/
    [Google Scholar]
  41. 41. 
    NIH OPA (Natl. Inst. Health Off. Portf. Anal.) 2020. iSearch COVID-19 portfolio Web Resour., NIH Bethesda, MD: https://icite.od.nih.gov/covid19/search/
    [Google Scholar]
  42. 42. 
    Zhao WM, Song SH, Chen ML, Zou D, Ma LN et al. 2020. The 2019 novel coronavirus resource. Yi Chuan 42:212–21
    [Google Scholar]
  43. 43. 
    Yang P, Fang H, Lin J. 2017. Anserini: enabling the use of Lucene for information retrieval research. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval1253–56 New York: Assoc. Comput. Mach.
    [Google Scholar]
  44. 44. 
    Raffel C, Shazeer N, Roberts A, Lee K, Narang S et al. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv:1910.10683 [cs.LG]
    [Google Scholar]
  45. 45. 
    Verspoor K, Šuster S, Otmakhova Y, Mendis S, Zhai Z et al. 2020. COVID-SEE: scientific evidence explorer for COVID-19 related research. arXiv:2008.07880 [cs.CL]
    [Google Scholar]
  46. 46. 
    Hope T, Portenoy J, Vasan K, Borchardt J, Horvitz E et al. 2020. SciSight: combining faceted navigation and research group detection for COVID-19 exploratory scientific search. arXiv:2005.12668 [cs.IR]
    [Google Scholar]
  47. 47. 
    Aizawa A, Bergeron F, Chen J, Cheng F, Hayashi K et al. 2020. A system for worldwide COVID-19 information aggregation. arXiv:2008.01523 [cs.CL]
    [Google Scholar]
  48. 48. 
    WHO (World Health Organ.) 2020. Global research on coronavirus disease (COVID-19) Tech. Rep, WHO Geneva: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov
    [Google Scholar]
  49. 49. 
    Roberts K, Alam T, Bedrick S, Demner-Fushman D, Lo K et al. 2020. TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19. J. Am. Med. Inform. Assoc. 27:91431–36
    [Google Scholar]
  50. 50. 
    Lee J, Yoon W, Kim S, Kim D, Kim S et al. 2020. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36:1234–40
    [Google Scholar]
  51. 51. 
    Fioranelli M, Sepehri A, Roccia MG, Jafferany M, Olisova OY et al. 2020. RETRACTED: 5G technology and induction of coronavirus in skin cells. J. Biol. Regul. Homeost. Agents 34:4 https://doi.org/10.23812/20-269-E-4R
    [Crossref] [Google Scholar]
  52. 52. 
    Brainard J. 2020. Scientists are drowning in COVID-19 papers. Can new tools keep them afloat?. Science May 13. https://www.sciencemag.org/news/2020/05/scientists-are-drowning-covid-19-papers-can-new-tools-keep-them-afloat
    [Google Scholar]
  53. 53. 
    Chan J, Oo S, Chor CYT, Yim D, Chan JSK, Harky A. 2020. COVID-19 and literature evidence: Should we publish anything and everything?. Acta Biomedica 91:e2020020
    [Google Scholar]
  54. 54. 
    Goulart RRV, de Lima VLS, Xavier CC. 2011. A systematic review of named entity recognition in biomedical texts. J. Braz. Comput. Soc. 17:103–16
    [Google Scholar]
  55. 55. 
    Li J, Sun Y, Johnson RJ, Sciaky D, Wei C-H et al. 2016. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database 2016:baw068
    [Google Scholar]
  56. 56. 
    Lu Z, Kao H-Y, Wei C-H, Huang M, Liu J et al. 2011. The gene normalization task in BioCreative III. BMC Bioinform. 12:S2
    [Google Scholar]
  57. 57. 
    Brown GR, Hem V, Katz KS, Ovetsky M, Wallin C et al. 2015. Gene: a gene-centered information resource at NCBI. Nucleic Acids Res 43:D36–42
    [Google Scholar]
  58. 58. 
    Wei C-H, Allot A, Leaman R, Lu Z. 2019. PubTator central: automated concept annotation for biomedical full text articles. Nucleic Acids Res 47:W587–93
    [Google Scholar]
  59. 59. 
    Wang X, Song X, Li B, Guan Y, Han J. 2020. Comprehensive named entity recognition on CORD-19 with distant or weak supervision. arXiv:2003.12218 [cs.CL]
    [Google Scholar]
  60. 60. 
    Wei C-H, Harris BR, Kao H-Y, Lu Z 2013. tmVar: a text mining approach for extracting sequence variants in biomedical literature. Bioinformatics 29:1433–39
    [Google Scholar]
  61. 61. 
    Lafferty J, McCallum A, Pereira FC. 2001. Conditional random fields: probabilistic models for segmenting and labeling sequence data. Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001)282–89 New York: Assoc. Comput. Mach.
    [Google Scholar]
  62. 62. 
    Leaman R, Lu Z. 2016. TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics 32:2839–46
    [Google Scholar]
  63. 63. 
    Huang Z, Xu W, Yu K 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991 [cs.CL]
    [Google Scholar]
  64. 64. 
    Devlin J, Chang M-W, Lee K, Toutanova K 2018. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 [cs.CL]
    [Google Scholar]
  65. 65. 
    Wang J, Pham HA, Manion F, Rouhizadeh M, Zhang Y. 2020. COVID-19 SignSym: a fast adaptation of general clinical NLP tools to identify and normalize COVID-19 signs and symptoms to OMOP common data model. arXiv:2007.10286 [cs.CL]
    [Google Scholar]
  66. 66. 
    Dong X, Li J, Soysal E, Bian J, DuVall SL et al. 2020. COVID-19 TestNorm: a tool to normalize COVID-19 testing names to LOINC codes. J. Am. Med. Inform. Assoc. 27:91437–42
    [Google Scholar]
  67. 67. 
    Colic N, Furrer L, Rinaldi F 2020. Annotating the pandemic: named entity recognition and normalisation in COVID-19 literature. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020 K Verspoor, KB Cohen, M Conway, B de Bruijn, M Dredze et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  68. 68. 
    Wei C-H, Harris BR, Li D, Berardini TZ, Huala E et al. 2012. Accelerating literature curation with text-mining tools: a case study of using PubTator to curate genes in PubMed abstracts. Database 2012:bas041
    [Google Scholar]
  69. 69. 
    Bekhuis T. 2006. Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy. J. Biomed. Digit. Libr. 3:2
    [Google Scholar]
  70. 70. 
    Swanson DR, Smalheiser NR. 1997. An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif. Intel. 91:183–203
    [Google Scholar]
  71. 71. 
    Gopalakrishnan V, Jha K, Jin W, Zhang A 2019. A survey on literature based discovery approaches in biomedical domain. J. Biomed. Inform. 93:103141
    [Google Scholar]
  72. 72. 
    Piad-Morffis A, Estevez-Velarde S, Estevanell-Valladares EL, Gutiérrez Y, Montoyo A et al. 2020. Knowledge discovery in COVID-19 research literature. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020 K Verspoor, KB Cohen, M Conway, B de Bruijn, M Dredze et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  73. 73. 
    Pinto BG, Oliveira AE, Singh Y, Jimenez L, Gonçalves ANA et al. 2020. ACE2 expression is increased in the lungs of patients with comorbidities associated with severe COVID-19. J. Infect. Dis. 222:4556–63
    [Google Scholar]
  74. 74. 
    Tarasova O, Ivanov S, Filimonov DA, Poroikov V. 2020. Data and text mining help identify key proteins involved in the molecular mechanisms shared by SARS-CoV-2 and HIV-1. Molecules 25:2944
    [Google Scholar]
  75. 75. 
    Karami A. 2020. Investigating diseases and chemicals in COVID-19 literature with text mining. JMIR Preprints 18/06/2020:21503 https://preprints.jmir.org/preprint/21503
    [Google Scholar]
  76. 76. 
    Zhang Y, Chen Q, Yang Z, Lin H, Lu Z. 2019. BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci. Data 6:52
    [Google Scholar]
  77. 77. 
    Beltagy I, Cohan A, Lo K. 2019. SciBERT: pretrained contextualized embeddings for scientific text. arXiv:1903.10676 [cs.CL]
    [Google Scholar]
  78. 78. 
    Martinc M, Škrlj B, Pirkmajer S, Lavrač N, Cestnik B et al. 2020. COVID-19 therapy target discovery with context-aware literature mining. arXiv:2007.15681 [cs.CL]
    [Google Scholar]
  79. 79. 
    Kuusisto F, Page D, Stewart R. 2020. Word embedding mining for SARS-CoV-2 and COVID-19 drug repurposing. F1000Research 9:585
    [Google Scholar]
  80. 80. 
    Tu J, Verhagen M, Cochran B, Pustejovsky J. 2020. Exploration and discovery of the COVID-19 literature through semantic visualization. arXiv:2007.01800 [cs.CL]
    [Google Scholar]
  81. 81. 
    Yeganova L, Islamaj R, Chen Q, Leaman R, Allot A et al. 2020. Navigating the landscape of COVID-19 research through literature analysis: a bird's eye view. arXiv:2008.03397 [cs.DL]
    [Google Scholar]
  82. 82. 
    Gates LE, Hamed AA. 2020. The anatomy of the SARS-CoV-2 biomedical literature: introducing the CovidX network algorithm for drug repurposing recommendation. J. Med. Internet Res. 22:e21169
    [Google Scholar]
  83. 83. 
    Patel JC, Tulswani R, Khurana P, Sharma YK, Ganju L et al. 2020. Identification of pulmonary comorbid diseases network based repurposing effective drugs for COVID-19. Res. Square. https://doi.org/10.21203/rs.3.rs-28148/v1
    [Crossref] [Google Scholar]
  84. 84. 
    Wang Q, Li M, Wang X, Parulian N, Han G et al. 2020. COVID-19 literature knowledge graph construction and drug repurposing report generation. arXiv:2007.00576 [cs.CL]
    [Google Scholar]
  85. 85. 
    Jurafsky D, Martin JH. 2009. Speech and Language Processing London: Pearson
    [Google Scholar]
  86. 86. 
    Athenikos SJ, Han H. 2010. Biomedical question answering: a survey. Comput. Methods Programs Biomed. 99:1–24
    [Google Scholar]
  87. 87. 
    Herriman M, Meer E, Rosin R, Lee V, Washington V et al. 2020. Asked and answered: building a chatbot to address Covid-19-related concerns. NEJM Catalyst Innovations in Care Delivery June 18. https://catalyst.nejm.org/doi/full/10.1056/cat.20.0230
    [Google Scholar]
  88. 88. 
    Wei J, Huang C, Vosoughi S, Wei J. 2020. What are people asking about COVID-19?. A question classification dataset arXiv:2005.12522 [cs.CL]
    [Google Scholar]
  89. 89. 
    McCreery CH, Katariya N, Kannan A, Chablani M, Amatriain X. 2020. Effective transfer learning for identifying similar questions: matching user questions to COVID-19 FAQs. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining3458–65 New York: Assoc. Comput. Mach.
    [Google Scholar]
  90. 90. 
    Li Y, Grandison T, Silveyra P, Douraghy A, Guan X et al. 2020. Jennifer for COVID-19: an NLP-powered chatbot built for the people and by the people to combat misinformation. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 K Verspoor, KB Cohen, M Dredze, E Ferrera, J May et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  91. 91. 
    Narayan S, Gardent C, Cohen SB, Shimorina A. 2017. Split and rephrase. arXiv:1707.06971 [cs.CL]
    [Google Scholar]
  92. 92. 
    Lee S, Kim D, Lee K, Choi J, Kim S et al. 2016. BEST: next-generation biomedical entity search tool for knowledge discovery from biomedical literature. PLOS ONE 11:e0164680
    [Google Scholar]
  93. 93. 
    Reimers N, Gurevych I. 2019. Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv:1908.10084 [cs.CL]
    [Google Scholar]
  94. 94. 
    Rajpurkar P, Zhang J, Lopyrev K, Liang P. 2016. SQuAD: 100,000+ questions for machine comprehension of text. arXiv:1606.05250 [cs.CL]
    [Google Scholar]
  95. 95. 
    Jin Q, Dhingra B, Liu Z, Cohen WW, Lu X. 2019. PubMedQA: a dataset for biomedical research question answering. arXiv:1909.06146 [cs.CL]
    [Google Scholar]
  96. 96. 
    Dong L, Yang N, Wang W, Wei F, Liu X et al. 2019. Unified language model pre-training for natural language understanding and generation. Advances in Neural Information Processing Systems13063–75 https://papers.nips.cc/paper/2019/hash/c20bb2d9a50d5ac1f713f8b34d9aac5a-Abstract.html
    [Google Scholar]
  97. 97. 
    Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A et al. 2019. BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv:1910.13461 [cs.CL]
    [Google Scholar]
  98. 98. 
    Esteva A, Kale A, Paulus R, Hashimoto K, Yin W et al. 2020. CO-Search: COVID-19 information retrieval with semantic search, question answering, and abstractive summarization. arXiv:2006.09595 [cs.IR]
    [Google Scholar]
  99. 99. 
    Doanvo A, Qian X, Ramjee D, Piontkivska H, Desai A, Majumder M. 2020. Machine learning maps research needs in COVID-19 literature. Patterns 1:9100123
    [Google Scholar]
  100. 100. 
    Wahbeh A, Nasralah T, Al-Ramahi M, El-Gayar O. 2020. Mining physicians' opinions on social media to obtain insights into COVID-19: mixed methods analysis. JMIR Public Health Surveill 6:e19276
    [Google Scholar]
  101. 101. 
    Moore RC, Lee A, Hancock JT, Halley M, Linos E. 2020. Experience with social distancing early in the COVID-19 pandemic in the United States: implications for public health messaging. medRxiv 2020.04.08.20057067. https://doi.org/10.1101/2020.04.08.20057067
    [Crossref] [Google Scholar]
  102. 102. 
    Debnath R, Bardhan R. 2020. India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling. PLOS ONE 15:e0238972
    [Google Scholar]
  103. 103. 
    Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S et al. 2020. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 7:547–60
    [Google Scholar]
  104. 104. 
    Li D, Chaudhary H, Zhang Z. 2020. Modeling spatiotemporal pattern of depressive symptoms caused by COVID-19 using social media data mining. Int. J. Environ. Res. Public Health 17:144988
    [Google Scholar]
  105. 105. 
    Low DM, Rumker L, Talker T, Torous J, Cecchi G, Ghosh SS. 2020. Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19: an observational study. J. Med. Internet Res. 22:10e22635
    [Google Scholar]
  106. 106. 
    Jelodar H, Wang Y, Orji R, Huang H. 2020. Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach. arXiv:2004.11695 [cs.IR]
    [Google Scholar]
  107. 107. 
    Aslam F, Awan TM, Syed JH, Kashif A, Parveen M. 2020. Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanit. Soc. Sci. Commun. 7:23
    [Google Scholar]
  108. 108. 
    de Las Heras-Pedrosa C, Sánchez-Núñez P, Peláez JI. 2020. Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems. Int. J. Environ. Res. Public Health 17:5542
    [Google Scholar]
  109. 109. 
    Drias HH, Drias Y. 2020. Mining Twitter data on COVID-19 for sentiment analysis and frequent patterns discovery. medRxiv 2020.05.08.20090464. https://doi.org/10.1101/2020.05.08.20090464
    [Crossref] [Google Scholar]
  110. 110. 
    Samuel J, Ali GG, Rahman M, Esawi E, Samuel Y. 2020. COVID-19 public sentiment insights and machine learning for tweets classification. Information 11:314
    [Google Scholar]
  111. 111. 
    Zhou J, Yang S, Xiao C, Chen F 2020. Examination of community sentiment dynamics due to COVID-19 pandemic: a case study from Australia. arXiv:2006.12185 [cs.SI]
    [Google Scholar]
  112. 112. 
    Ahmed ME, Rabin MRI, Chowdhury FN. 2020. COVID-19: social media sentiment analysis on reopening. arXiv:2006.00804 [cs.SI]
    [Google Scholar]
  113. 113. 
    Han X, Wang J, Zhang M, Wang X. 2020. Using social media to mine and analyze public opinion related to COVID-19 in China. Int. J. Environ. Res. Public Health 17:2788
    [Google Scholar]
  114. 114. 
    Wagner T, Shweta F, Murugadoss K, Awasthi S, Venkatakrishnan AJ et al. 2020. Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis. eLife 9:e58227
    [Google Scholar]
  115. 115. 
    Callahan A, Steinberg E, Fries JA, Gombar S, Patel B et al. 2020. Estimating the efficacy of symptom-based screening for COVID-19. NPJ Digit. Med. 3:95
    [Google Scholar]
  116. 116. 
    Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S et al. 2018. CLAMP—a toolkit for efficiently building customized clinical natural language processing pipelines. J. Am. Med. Inform. Assoc. 25:331–36
    [Google Scholar]
  117. 117. 
    Wang J, Pham HA, Manion F, Rouhizadeh M, Zhang Y. 2020. COVID-19 SignSym: a fast adaptation of general clinical NLP tools to identify and normalize COVID-19 signs and symptoms to OMOP common data model. arXiv:2007.10286 [cs.CL]
    [Google Scholar]
  118. 118. 
    Chapman AB, Peterson KS, Turano A, Box TL, Wallace KS, Jones M 2020. A natural language processing system for national COVID-19 surveillance in the US Department of Veterans Affairs. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 K Verspoor, KB Cohen, M Dredze, E Ferrera, J May et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  119. 119. 
    Fries JA, Steinberg E, Khattar S, Fleming SL, Posada J et al. 2020. Trove: ontology-driven weak supervision for medical entity classification. arXiv:2008.01972 [cs.CL]
    [Google Scholar]
  120. 120. 
    Picone M, Inoue S, DeFelice C, Naujokas MF, Sinrod J et al. 2020. Social listening as a rapid approach to collecting and analyzing COVID-19 symptoms and disease natural histories reported by large numbers of individuals. Popul. Health Manag. 23:5350–60
    [Google Scholar]
  121. 121. 
    Shen C, Chen A, Luo C, Zhang J, Feng B, Liao W. 2020. Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in mainland China: observational infoveillance study. J. Med. Internet Res. 22:e19421
    [Google Scholar]
  122. 122. 
    Zheng N, Du S, Wang J, Zhang H, Cui W et al. 2020. Predicting COVID-19 in China using hybrid AI model. IEEE Trans. Cybern. 50:2891–904
    [Google Scholar]
  123. 123. 
    Li L, Zhang Q, Wang X, Zhang J, Wang T et al. 2020. Characterizing the propagation of situational information in social media during COVID-19 epidemic: a case study on Weibo. IEEE Trans. Comput. Soc. Syst. 7:556–62
    [Google Scholar]
  124. 124. 
    Lee N, Bang Y, Madotto A, Fung P. 2020. Misinformation has high perplexity. arXiv:2006.04666 [cs.CL]
    [Google Scholar]
  125. 125. 
    Elhadad MK, Li KF, Gebali F 2021. An ensemble deep learning technique to detect COVID-19 misleading information. Advances in Network-Based Information Systems L Barolli, KF Li, T Enokido, M Takizawa 163–75 Cham, Switz: Springer Int.
    [Google Scholar]
  126. 126. 
    Serrano JCM, Papakyriakopoulos O, Hegelich S 2020. NLP-based feature extraction for the detection of COVID-19 misinformation videos on YouTube. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 K Verspoor, KB Cohen, M Dredze, E Ferrera, J May et al. Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  127. 127. 
    Groza A. 2020. Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology. arXiv:2004.12330 [cs.AI]
    [Google Scholar]
  128. 128. 
    Cui L, Lee D. 2020. CoAID: COVID-19 healthcare misinformation dataset. arXiv:2006.00885 [cs.SI]
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-021821-061045
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