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利用社交媒体数据对 COVID-19 匿名医生观点进行描述。

Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data.

机构信息

Data Science to Patient Value, University of Colorado School of Medicine, Aurora, CO 80045, USA* Corresponding author,

出版信息

Pac Symp Biocomput. 2021;26:95-106.

Abstract

Physicians' beliefs and attitudes about COVID-19 are important to ascertain because of their central role in providing care to patients during the pandemic. Identifying topics and sentiments discussed by physicians and other healthcare workers can lead to identification of gaps relating to theCOVID-19 pandemic response within the healthcare system. To better understand physicians' perspectives on the COVID-19 response, we extracted Twitter data from a specific user group that allows physicians to stay anonymous while expressing their perspectives about the COVID-19 pandemic. All tweets were in English. We measured most frequent bigrams and trigrams, compared sentiment analysis methods, and compared our findings to a larger Twitter dataset containing general COVID-19 related discourse. We found significant differences between the two datasets for specific topical phrases. No statistically significant difference was found in sentiments between the two datasets, and both trended slightly more positive than negative. Upon comparison to manual sentiment analysis, it was determined that these sentiment analysis methods should be improved to accurately capture sentiments of anonymous physician data. Anonymous physician social media data is a unique source of information that provides important insights into COVID-19 perspectives.

摘要

医生对 COVID-19 的信念和态度很重要,因为他们在大流行期间为患者提供护理方面发挥着核心作用。确定医生和其他医疗保健工作者讨论的主题和情绪,可以发现医疗保健系统中与 COVID-19 大流行应对相关的差距。为了更好地了解医生对 COVID-19 应对措施的看法,我们从一个特定的用户群体中提取了 Twitter 数据,该用户群体允许医生在表达对 COVID-19 大流行的看法时保持匿名。所有推文均为英文。我们测量了最常见的双词和三词,比较了情感分析方法,并将我们的发现与包含一般 COVID-19 相关论述的更大的 Twitter 数据集进行了比较。我们发现两个数据集之间对于特定主题短语存在显着差异。两个数据集之间的情绪没有统计学上的显着差异,而且都略微偏向积极。与手动情感分析相比,确定这些情感分析方法需要改进,以准确捕捉匿名医生数据的情感。匿名医生社交媒体数据是一种独特的信息来源,可以深入了解 COVID-19 的观点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0bb/7958992/e9bd03bb8c42/nihms-1649360-f0001.jpg

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