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利用标签#scamdemic 和 #plandemic 分析推特上的 COVID-19 虚假信息:回顾性研究。

Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study.

机构信息

Clinical Informatics Center, UT Southwestern Medical Center, Dallas, Texas, United States of America.

出版信息

PLoS One. 2022 Jun 22;17(6):e0268409. doi: 10.1371/journal.pone.0268409. eCollection 2022.

Abstract

INTRODUCTION

The use of social media during the COVID-19 pandemic has led to an "infodemic" of mis- and disinformation with potentially grave consequences. To explore means of counteracting disinformation, we analyzed tweets containing the hashtags #Scamdemic and #Plandemic.

METHODS

Using a Twitter scraping tool called twint, we collected 419,269 English-language tweets that contained "#Scamdemic" or "#Plandemic" posted in 2020. Using the Twitter application-programming interface, we extracted the same tweets (by tweet ID) with additional user metadata. We explored descriptive statistics of tweets including their content and user profiles, analyzed sentiments and emotions, performed topic modeling, and determined tweet availability in both datasets.

RESULTS

After removal of retweets, replies, non-English tweets, or duplicate tweets, 40,081 users tweeted 227,067 times using our selected hashtags. The mean weekly sentiment was overall negative for both hashtags. One in five users who used these hashtags were suspended by Twitter by January 2021. Suspended accounts had an average of 610 followers and an average of 6.7 tweets per user, while active users had an average of 472 followers and an average of 5.4 tweets per user. The most frequent tweet topic was "Complaints against mandates introduced during the pandemic" (79,670 tweets), which included complaints against masks, social distancing, and closures.

DISCUSSION

While social media has democratized speech, it also permits users to disseminate potentially unverified or misleading information that endangers people's lives and public health interventions. Characterizing tweets and users that use hashtags associated with COVID-19 pandemic denial allowed us to understand the extent of misinformation. With the preponderance of inaccessible original tweets, we concluded that posters were in denial of the COVID-19 pandemic and sought to disperse related mis- or disinformation resulting in suspension.

CONCLUSION

Leveraging 227,067 tweets with the hashtags #scamdemic and #plandemic in 2020, we were able to elucidate important trends in public disinformation about the COVID-19 vaccine.

摘要

简介

在 COVID-19 大流行期间使用社交媒体导致了错误和虚假信息的“信息疫情”,可能产生严重后果。为了探索对抗虚假信息的方法,我们分析了包含标签#Scamdemic 和#Plandemic 的推文。

方法

使用名为 twint 的 Twitter 爬虫工具,我们收集了 2020 年发布的包含#Scamdemic 或#Plandemic 的 419,269 条英语推文。使用 Twitter 的应用程序编程接口,我们通过推文 ID 提取了相同的推文及其额外的用户元数据。我们探索了推文的描述性统计数据,包括其内容和用户资料,分析了情绪和情感,进行了主题建模,并确定了两个数据集中文本的可用性。

结果

去除转发、回复、非英语推文或重复推文后,40,081 名用户使用我们选择的标签共发布了 227,067 条推文。这两个标签的每周平均情绪都是负面的。到 2021 年 1 月,使用这些标签的五分之一用户被 Twitter 暂停。被暂停的账户平均有 610 个关注者,每个用户平均有 6.7 条推文,而活跃用户平均有 472 个关注者,每个用户平均有 5.4 条推文。最常见的推文主题是“对大流行期间引入的强制接种疫苗的投诉”(79,670 条推文),其中包括对口罩、社交距离和关闭措施的投诉。

讨论

虽然社交媒体使言论民主化,但它也允许用户传播未经证实或具有误导性的信息,从而危及人们的生命和公共卫生干预措施。对使用与 COVID-19 大流行否认相关标签的推文和用户进行特征描述,使我们能够了解虚假信息的程度。由于原始推文大多无法访问,我们得出的结论是,发布者否认 COVID-19 大流行的存在,并试图散布相关的错误或虚假信息,从而导致了暂停。

结论

利用 2020 年包含标签#scamdemic 和#plandemic 的 227,067 条推文,我们能够阐明有关 COVID-19 疫苗的公众虚假信息的重要趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6742/9216575/9220ef45e0c4/pone.0268409.g001.jpg

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