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推特上拉丁美洲主要媒体对新冠疫苗的情绪分析:阿根廷、智利、哥伦比亚、墨西哥和秘鲁的情况

Sentiment Analysis toward the COVID-19 Vaccine in the Main Latin American Media on Twitter: The Cases of Argentina, Chile, Colombia, Mexico, and Peru.

作者信息

Córdoba-Cabús Alba, García-Borrego Manuel, Ceballos Yaiza

机构信息

Department of Journalism, Faculty of Communication Sciences, University of Malaga, 29071 Málaga, Spain.

出版信息

Vaccines (Basel). 2023 Oct 14;11(10):1592. doi: 10.3390/vaccines11101592.

DOI:10.3390/vaccines11101592
PMID:37896994
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10610635/
Abstract

This article analyzes the media coverage of the COVID-19 vaccine by major media outlets in five Latin American countries: Argentina, Colombia, Chile, Mexico, and Peru. For this purpose, the XLM-roBERTa model was applied and the sentiments of all tweets published between January 2020 and June 2023 ( = 24,243) by the five outlets with the greatest online reach in each country were analyzed. The results show that the sentiment in the overall media and in each nation studied was mostly negative, and only at the beginning of the pandemic was there some positivity. In recent months, negative sentiment has increased twelvefold over positive sentiment, and has also garnered many more interactions than positive sentiment. The differences by platform and country are minimal, but there are markedly negative media, some more inclined to neutrality, and only one where positive sentiment predominates. This paper questions the role of journalism in Latin America during a health crisis as serious as that of the coronavirus, in which, instead of the expected neutrality, or even a certain message of hope, the media seem to have been dragged along by the negativity promoted by certain discourses far removed from scientific evidence.

摘要

本文分析了阿根廷、哥伦比亚、智利、墨西哥和秘鲁这五个拉丁美洲国家主要媒体对新冠疫苗的报道。为此,应用了XLM-roBERTa模型,并分析了每个国家网络影响力最大的五家媒体在2020年1月至2023年6月期间发布的所有推文(共24243条)的情绪。结果表明,总体媒体以及所研究的每个国家的情绪大多为负面,仅在疫情初期存在一些积极情绪。近几个月来,负面情绪比积极情绪增加了12倍,并且获得的互动也比积极情绪多得多。不同平台和国家之间的差异很小,但存在明显负面的媒体,有些更倾向于中立,只有一家媒体以积极情绪为主导。本文质疑在像新冠病毒这样严重的健康危机期间拉丁美洲新闻业的作用,在这场危机中,媒体似乎被某些远离科学证据的负面言论所左右,而没有达到预期的中立,甚至没有传达某种希望的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/592e74930ce6/vaccines-11-01592-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/ec1f6c95cea2/vaccines-11-01592-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/0f0846a5d488/vaccines-11-01592-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/877469d035e9/vaccines-11-01592-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/45bc94d7aa92/vaccines-11-01592-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/592e74930ce6/vaccines-11-01592-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/ec1f6c95cea2/vaccines-11-01592-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/0f0846a5d488/vaccines-11-01592-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/877469d035e9/vaccines-11-01592-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/45bc94d7aa92/vaccines-11-01592-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb9/10610635/592e74930ce6/vaccines-11-01592-g005.jpg

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本文引用的文献

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Vaccine. 2023 May 11;41(20):3196-3203. doi: 10.1016/j.vaccine.2023.03.068. Epub 2023 Apr 4.
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[COVID-19 in Latin America: a systematic review and bibliometric analysis].[拉丁美洲的2019冠状病毒病:系统评价与文献计量分析]
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COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies.
COVID-19 公众意见:使用机器学习方法的 Twitter 医疗保健数据处理。
Int J Environ Res Public Health. 2022 Dec 27;20(1):432. doi: 10.3390/ijerph20010432.
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Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.量化新冠疫情导致的主流媒体中疫苗接种覆盖率变化:文本挖掘研究
JMIR Infodemiology. 2022 Sep 20;2(2):e35121. doi: 10.2196/35121. eCollection 2022 Jul-Dec.
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Bots' Activity on COVID-19 Pro and Anti-Vaccination Networks: Analysis of Spanish-Written Messages on Twitter.机器人在新冠疫情支持与反对疫苗接种网络上的活动:对推特上西班牙语推文的分析
Vaccines (Basel). 2022 Aug 2;10(8):1240. doi: 10.3390/vaccines10081240.
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