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本文引用的文献

1
It doesn't take a village to fall for misinformation: Social media use, discussion heterogeneity preference, worry of the virus, faith in scientists, and COVID-19-related misinformation beliefs.轻信错误信息并非众人皆会:社交媒体使用、讨论异质性偏好、对病毒的担忧、对科学家的信任以及与新冠疫情相关的错误信息信念。
Telemat Inform. 2021 May;58:101547. doi: 10.1016/j.tele.2020.101547. Epub 2020 Dec 4.
2
CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter.CoAID-DEEP:用于自动检测推特上新冠病毒误导性信息的优化智能框架
IEEE Access. 2021 Feb 9;9:27840-27867. doi: 10.1109/ACCESS.2021.3058066. eCollection 2021.
3
Battling the COVID-19 infodemic in an Irish context: the role of iHealthFacts.在爱尔兰背景下抗击新冠疫情信息疫情:iHealthFacts的作用。
HRB Open Res. 2020 Nov 9;3:81. doi: 10.12688/hrbopenres.13174.1. eCollection 2020.
4
COVID-19 as an "Infodemic" in Public Health: Critical Role of the Social Media.新型冠状病毒肺炎疫情期间的信息洪流对公共卫生的影响:社交媒体的关键作用。
Front Public Health. 2021 Mar 18;9:610623. doi: 10.3389/fpubh.2021.610623. eCollection 2021.
5
Socioeconomic Disparities in eHealth Literacy and Preventive Behaviors During the COVID-19 Pandemic in Hong Kong: Cross-sectional Study.**题目**:在香港,新冠疫情期间电子健康素养和预防行为的社会经济差异:一项横断面研究。 **解析**:原文“Socioeconomic Disparities in eHealth Literacy and Preventive Behaviors During the COVID-19 Pandemic in Hong Kong: Cross-sectional Study.”是一个较长的英文句子,翻译时需要注意断句和中文表达习惯。“eHealth Literacy”翻译为“电子健康素养”,“preventive behaviors”翻译为“预防行为”。
J Med Internet Res. 2021 Apr 14;23(4):e24577. doi: 10.2196/24577.
6
COVID-19 Vaccination and the Challenge of Infodemic and Disinformation.2019冠状病毒病疫苗接种与信息疫情和虚假信息的挑战。
J Korean Med Sci. 2021 Mar 15;36(10):e78. doi: 10.3346/jkms.2021.36.e78.
7
Online mis/disinformation and vaccine hesitancy in the era of COVID-19: Why we need an eHealth literacy revolution.新冠疫情时代的网络错误/虚假信息和疫苗犹豫:为什么我们需要一场电子健康素养革命。
Hum Vaccin Immunother. 2022 Dec 31;18(1):1-3. doi: 10.1080/21645515.2021.1874218. Epub 2021 Feb 24.
8
CoVerifi: A COVID-19 news verification system.CoVerifi:一个新冠疫情新闻核实系统。
Online Soc Netw Media. 2021 Mar;22:100123. doi: 10.1016/j.osnem.2021.100123. Epub 2021 Jan 23.
9
Prevalence of Health Misinformation on Social Media: Systematic Review.社交媒体健康类错误信息的流行情况:系统评价。
J Med Internet Res. 2021 Jan 20;23(1):e17187. doi: 10.2196/17187.
10
COVID-19 information-related digital literacy among online health consumers in a low-income country.低收入国家在线健康消费者的 COVID-19 信息相关数字素养。
Int J Med Inform. 2021 Jan;145:104322. doi: 10.1016/j.ijmedinf.2020.104322. Epub 2020 Nov 1.

新冠病毒在线错误信息与健康素养:简要概述。

COVID-19 Misinformation Online and Health Literacy: A Brief Overview.

机构信息

Department of Library & Information Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan.

Information Management School, Sun Yat-sen University, Guangzhou 510006, China.

出版信息

Int J Environ Res Public Health. 2021 Jul 30;18(15):8091. doi: 10.3390/ijerph18158091.

DOI:10.3390/ijerph18158091
PMID:34360384
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8345771/
Abstract

Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and 'viral' nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.

摘要

数字健康素养低下影响着全球很大一部分人群,是 COVID-19 相关网络错误信息(与机器人共同作用)传播的直接原因。社交媒体分享的便利性和“病毒性”进一步使情况复杂化。本文简要概述了社交媒体上 COVID-19 错误信息的问题规模、其破坏性影响,以及其与数字健康素养的复杂关系。然后,简要回顾了可用于检测和预防 COVID-19 错误信息传播的主要策略、方法和服务,包括基于机器学习的方法、健康素养指南、清单、破除迷思和事实核查。鉴于 COVID-19 信息疫情的复杂性,任何这些方法或工具都不太可能单独有效地阻止 COVID-19 错误信息的传播。相反,强烈建议采用混合、协同的方法,将这些策略、方法和服务的优点结合起来,以解决网络健康错误信息问题,并减轻其在 COVID-19 和未来大流行中的负面影响。此外,技术和工具应理想地专注于评估信息内容和信息发布者,并不仅仅依靠评估后者作为对前者可信度和真实性的快速简便代理。调查和提高人口数字健康素养水平对于未来的信息疫情准备也是必不可少的。