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本文引用的文献

1
COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter.COVID-Twitter-BERT：一种用于分析推特上新冠疫情相关内容的自然语言处理模型。
Front Artif Intell. 2023 Mar 14;6:1023281. doi: 10.3389/frai.2023.1023281. eCollection 2023.
2
Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets.发现长期新冠症状模式：社交媒体推文的关联规则挖掘与情感分析
JMIR Form Res. 2022 Sep 7;6(9):e37984. doi: 10.2196/37984.
3
Discovery of COVID-19 Symptomatic Experience Reported by Twitter Users.推特用户报告的 COVID-19 症状体验的发现。
Stud Health Technol Inform. 2022 May 25;294:664-668. doi: 10.3233/SHTI220552.
4
Insights from Twitter about novel COVID-19 symptoms.来自推特的关于新型冠状病毒肺炎（COVID-19）新症状的见解。
Eur Heart J Digit Health. 2020 Nov 23;1(1):4-5. doi: 10.1093/ehjdh/ztaa003. eCollection 2020 Nov.
5
Exploring experiences of COVID-19-positive individuals from social media posts.通过社交媒体帖子探索新冠病毒检测呈阳性者的经历。
Int J Nurs Pract. 2021 Oct;27(5):e12986. doi: 10.1111/ijn.12986. Epub 2021 Jun 14.
6
Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource.在 Twitter 上自我报告的 COVID-19 症状：分析与研究资源。
J Am Med Inform Assoc. 2020 Aug 1;27(8):1310-1315. doi: 10.1093/jamia/ocaa116.
7
Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.基于机器学习的方法在推特上检测与 COVID-19 相关的自我报告症状、检测途径和康复情况：回顾性大数据信息监测研究。
JMIR Public Health Surveill. 2020 Jun 8;6(2):e19509. doi: 10.2196/19509.

Automatically Identifying Self-Reports of COVID-19 Diagnosis on Twitter: An Annotated Data Set, Deep Neural Network Classifiers, and a Large-Scale Cohort.

作者信息

Klein Ari Z, Kunatharaju Shriya, O'Connor Karen, Gonzalez-Hernandez Graciela

机构信息

Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Autism Spectrum Program of Excellence, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

出版信息

J Med Internet Res. 2023 Jul 3;25:e46484. doi: 10.2196/46484.

DOI:10.2196/46484
PMID:37399062
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10365612/
Abstract
摘要