Dynamic Topic Modeling to Mine Themes and Evolution during the Initial COVID-19 Vaccine Rollout

IF 0.5 Q4 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Amkita Agarwal, Emily Burwell, W. Romine, Tanvi Banerjee
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引用次数: 0

Abstract

Objective: In this paper, we identify the topics in the form of themes being discussed on Twitter about the COVID-19 vaccine during the period of initial rollout of the vaccines and their evolution every month with the scientific advancement and major events on the COVID-19 vaccine timeline. Methods: We collected tweets from Twitter API over a period of 3 months from December 1, 2020 to February 28, 2021 using the keyword, "COVID-19 vaccine: and implemented dynamic topic modeling to identify topics in the form of themes being discussed. We then visualized the evolution of these themes every month with the news events during that time. Results: We found that 8 themes were discussed on Twitter during the initial rollout of COVID-19 vaccines. Within each theme, there were some unique key words found in a particular month or new key words that emerged from the previous month. These themes evolved with the trending news during that time period. Conclusions: Public discussions and health behavior of people about COVID-19 vaccines across different themes evolved over time. There was a dynamic and temporal shift in the perception of people regarding these vaccines coinciding with the scientific advancement and news events around the development, distribution, and administration of these vaccines.
动态主题建模以挖掘COVID-19疫苗初始推出期间的主题和演变
目的:在本文中,我们以Twitter上讨论的关于COVID-19疫苗的主题的形式确定疫苗最初推出期间的主题,以及每个月随着科学进展和COVID-19疫苗时间表上的重大事件的演变。方法:以“COVID-19疫苗”为关键词,从2020年12月1日至2021年2月28日的3个月内,从Twitter API中收集推文,并实施动态主题建模,以正在讨论的主题形式识别主题。然后,我们将每个月这些主题的演变与那段时间的新闻事件可视化。结果:我们发现,在COVID-19疫苗最初推出期间,推特上讨论了8个主题。在每个主题中,都有一些特定月份发现的独特关键词或上个月出现的新关键词。这些主题随着那段时间的趋势新闻而演变。结论:随着时间的推移,不同主题的公众对COVID-19疫苗的讨论和人们的健康行为发生了变化。人们对这些疫苗的看法随着科学进步和围绕这些疫苗的开发、分发和管理的新闻事件而发生了动态和短暂的转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Health Behavior and Policy Review
Health Behavior and Policy Review PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
1.40
自引率
12.50%
发文量
37
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