Suppr超能文献

冠状病毒迅速传播:量化推特上关于新冠疫情的错误信息传播情况

Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter.

作者信息

Kouzy Ramez, Abi Jaoude Joseph, Kraitem Afif, El Alam Molly B, Karam Basil, Adib Elio, Zarka Jabra, Traboulsi Cindy, Akl Elie W, Baddour Khalil

机构信息

Faculty of Medicine, American University of Beirut, Beirut, LBN.

Faculty of Medicine, American Univeristy of Beirut, Beirut, LBN.

出版信息

Cureus. 2020 Mar 13;12(3):e7255. doi: 10.7759/cureus.7255.

Abstract

Background Since the beginning of the coronavirus disease 2019 (COVID-19) epidemic, misinformation has been spreading uninhibited over traditional and social media at a rapid pace. We sought to analyze the magnitude of misinformation that is being spread on Twitter (Twitter, Inc., San Francisco, CA) regarding the coronavirus epidemic.  Materials and methods We conducted a search on Twitter using 14 different trending hashtags and keywords related to the COVID-19 epidemic. We then summarized and assessed individual tweets for misinformation in comparison to verified and peer-reviewed resources. Descriptive statistics were used to compare terms and hashtags, and to identify individual tweets and account characteristics. Results The study included 673 tweets. Most tweets were posted by informal individuals/groups (66%), and 129 (19.2%) belonged to verified Twitter accounts. The majority of included tweets contained serious content (91.2%); 548 tweets (81.4%) included genuine information pertaining to the COVID-19 epidemic. Around 70% of the tweets tackled medical/public health information, while the others were pertaining to sociopolitical and financial factors. In total, 153 tweets (24.8%) included misinformation, and 107 (17.4%) included unverifiable information regarding the COVID-19 epidemic. The rate of misinformation was higher among informal individual/group accounts (33.8%, p: <0.001). Tweets from unverified Twitter accounts contained more misinformation (31.0% vs 12.6% for verified accounts, p: <0.001). Tweets from healthcare/public health accounts had the lowest rate of unverifiable information (12.3%, p: 0.04). The number of likes and retweets per tweet was not associated with a difference in either false or unverifiable content. The keyword "COVID-19" had the lowest rate of misinformation and unverifiable information, while the keywords "#2019_ncov" and "Corona" were associated with the highest amount of misinformation and unverifiable content respectively. Conclusions Medical misinformation and unverifiable content pertaining to the global COVID-19 epidemic are being propagated at an alarming rate on social media. We provide an early quantification of the magnitude of misinformation spread and highlight the importance of early interventions in order to curb this phenomenon that endangers public safety at a time when awareness and appropriate preventive actions are paramount.

摘要

背景 自2019冠状病毒病(COVID-19)疫情开始以来,错误信息在传统媒体和社交媒体上肆意快速传播。我们试图分析在推特(Twitter公司,加利福尼亚州旧金山)上传播的关于冠状病毒疫情的错误信息的规模。

材料与方法 我们使用14个与COVID-19疫情相关的不同热门标签和关键词在推特上进行搜索。然后,我们将每条推文与经过验证和同行评审的资源进行比较,总结并评估其中的错误信息。使用描述性统计来比较术语和标签,并识别每条推文及账户特征。

结果 该研究纳入了673条推文。大多数推文由非正式个人/团体发布(66%),129条(19.2%)属于经过验证的推特账户。纳入的推文中大多数包含严肃内容(91.2%);548条推文(81.4%)包含与COVID-19疫情相关的真实信息。大约70%的推文涉及医学/公共卫生信息,其他推文则涉及社会政治和经济因素。总共有153条推文(24.8%)包含错误信息,107条(17.4%)包含关于COVID-19疫情的无法核实的信息。非正式个人/团体账户中的错误信息发生率更高(33.8%,p:<0.001)。来自未经验证的推特账户的推文包含更多错误信息(未经验证账户为31.0%,经过验证的账户为12.6%,p:<0.001)。来自医疗保健/公共卫生账户的推文无法核实信息的发生率最低(12.3%,p:0.04)。每条推文的点赞数和转发数与虚假或无法核实内容的差异无关。关键词“COVID-19”的错误信息和无法核实信息的发生率最低,而关键词“#2019_ncov”和“Corona”分别与最高数量的错误信息和无法核实内容相关。

结论 关于全球COVID-19疫情的医学错误信息和无法核实的内容在社交媒体上正以惊人的速度传播。我们对传播的错误信息规模进行了早期量化,并强调了早期干预的重要性,以便在意识和适当预防行动至关重要的时刻遏制这种危及公众安全的现象。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b6a/7152572/aa10efee80c2/cureus-0012-00000007255-i01.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验