分析推特上关于Covid-19疫苗副作用的对话

Atharva Manjrekar, Devin J. McConnell, S. Gokhale
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引用次数: 1

摘要

本文挖掘了在Covid-19疫苗推出早期阶段使用“副作用”收集的社交媒体对话。多维度分析旨在揭示疫苗接种后的症状及其强度,潜在的潜在情绪,话语如何传播和接受,并最终根据其严重程度将内容分为两组。这篇论文推断,人们会用夸张的情绪来分享造成重大不便和混乱的严重症状,这些作者的追随者网络更强大。概述轻微症状的推文被点赞和转发了很多次,这些作者的朋友网络也更强大。两种类型的推文(严重和轻微)都表达了严重的惊讶和一些恐惧。尝试使用boosting、神经网络和基于bert的自然语言转换方法将推文分为轻度和重度两类。BERT显著优于前两者,f1得分为0.88。鉴于新冠肺炎疫苗开发和部署迅速,有关其副作用的临床数据有限,这项研究填补了在现实环境中收集此类信息的重要空白。因此,本文最后讨论了公共卫生组织如何利用这些发现来对抗疫苗犹豫和最大限度地利用疫苗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Twitter Conversations on Side Effects of Covid-19 Vaccine
This paper mines social media conversations collected using #sideeffects during the early phases of the Covid-19 vaccine roll out. The multi-dimensional analysis seeks to uncover post-vaccination symptoms and their intensity, latent underlying emotions, how the discourse spreads and is received, and ultimately separate the content into two groups according to their severity. The paper infers that people share their severe symptoms that cause major inconvenience and disruption using exaggerated emotions, and the follower networks of these authors are stronger. Tweets outlining mild symptoms are liked and retweeted many more times, and the friends networks of these authors are stronger. Both types of tweets (severe and mild) express heavy surprise and some fear. Classification of the tweets into mild and severe groups is attempted using boosting, neural network and BERT-based natural language transformer methods. BERT significantly outperforms the former two achieving a F1-score of 0.88. Given that the Covid-19 vaccine was developed and deployed rapidly, with limited clinical data on its side effects, this research fills an important gap in gathering such information under real-life settings. The paper thus concludes with a discussion of how these findings could be leveraged by public health organizations to combat vaccine hesitancy and maximize vaccine uptake.
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