TY -非盟的陈,艾米丽盟——江,朱莉AU - Chang Ho-Chun赫伯特盟——Muric Goran AU -费拉拉,埃米利奥PY - 2022 DA - 2022/2/8 TI -图表的信息和错误信息景观描述Misinfodemics社交媒体:COVID-19 Infodemiology研究全球范围乔- JMIR Infodemiology SP - e32378六世- 2 - 1 KW -社会媒体KW -社交网络KW - Twitter千瓦COVID-19 KW -有关千瓦Misinfodemics KW - Infodemiology KW -错误AB -背景:自2020年初以来，这种新型冠状病毒，也被称为SARS-CoV-2，已经定义了我们的大部分生活。在此期间，世界各国实施了封锁和社会距离措施。人们的身体活动逐渐停止，而他们的在线互动增加了，因为他们转向了虚拟互动。随着交流方式的转移，信息消费也转移到了网上。管理当局和卫生机构有意将重点转移到利用社交媒体和在线平台传播事实和及时的信息。然而，这也为错误信息打开了大门，助长并加速了错误信息传染的现象。目的:我们对一年来与COVID-19相关的超过10亿条推文的推文进行了分析，以识别和调查普遍存在的错误信息叙述和趋势。我们还旨在描述更容易受到健康相关错误信息影响的Twitter受众，以及推动错误信息传播的网络机制。方法:我们利用了我们收集并公开的数据集，其中包含2020年1月至2021年4月期间与COVID-19相关的超过10亿条推文。 We created a subset of this larger data set by isolating tweets that included URLs with domains that had been identified by Media Bias/Fact Check as being prone to questionable and misinformation content. By leveraging clustering and topic modeling techniques, we identified major narratives, including health misinformation and conspiracies, which were present within this subset of tweets. Results: Our focus was on a subset of 12,689,165 tweets that we determined were representative of COVID-19 misinformation narratives in our full data set. When analyzing tweets that shared content from domains known to be questionable or that promoted misinformation, we found that a few key misinformation narratives emerged about hydroxychloroquine and alternative medicines, US officials and governing agencies, and COVID-19 prevention measures. We further analyzed the misinformation retweet network and found that users who shared both questionable and conspiracy-related content were clustered more closely in the network than others, supporting the hypothesis that echo chambers can contribute to the spread of health misinfodemics. Conclusions: We presented a summary and analysis of the major misinformation discourse surrounding COVID-19 and those who promoted and engaged with it. While misinformation is not limited to social media platforms, we hope that our insights, particularly pertaining to health-related emergencies, will help pave the way for computational infodemiology to inform health surveillance and interventions. SN - 2564-1891 UR - https://infodemiology.www.mybigtv.com/2022/1/e32378 UR - https://doi.org/10.2196/32378 UR - http://www.ncbi.nlm.nih.gov/pubmed/35190798 DO - 10.2196/32378 ID - info:doi/10.2196/32378 ER -