The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement.
The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement.
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DOI：
10.1109/access.2021.3059821
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Tajariol F
中科院分区：
文献类型：
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作者：
Cotfas LA;Delcea C;Roxin I;Ioanas C;Gherai DS;Tajariol F
新型冠状病毒爆发带来前所未有的措施，迫使当局作出有关在受疫情影响最严重的地区恢复封锁的决定。社交媒体一直是人们度过这一困难时期的重要支持。2020年11月9日，当第一种有效率超过90%的疫苗公布时，社交媒体已经做出反应，世界各地的人们开始表达他们对疫苗接种的感受，这不再是一个假设，而是每天都在接近成为现实。本文件旨在分析有关COVID-19疫苗接种的意见动态，考虑自首次疫苗公布后至英国首次接种疫苗的一个月期间，民间社会对疫苗接种过程表现出较高的兴趣。经典的机器学习和深度学习算法进行了比较，以选择性能最好的分类器。2 349 659条推文已被收集、分析，并与媒体报道的事件联系起来。基于分析，可以观察到大多数推文具有中立立场，而赞成推文的数量超过反对推文的数量。至于新闻，据观察，推文的发生遵循事件的趋势。更重要的是，所提出的方法可以用于更长的监测活动，可以帮助政府创建适当的通信手段，并对其进行评估，以便向公众提供明确和充分的信息，这可以增加公众对疫苗接种活动的信任。
The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.