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意大利推特疫苗对话在大流行第一阶段如何变化:混合方法分析。

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis.

机构信息

Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.

Department of Human Sciences, Link Campus University, Rome, Italy.

出版信息

Front Public Health. 2022 May 18;10:824465. doi: 10.3389/fpubh.2022.824465. eCollection 2022.

DOI:10.3389/fpubh.2022.824465
PMID:35664110
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9157769/
Abstract

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.

摘要

在欧洲联合疫苗行动的背景下,我们通过定量和定性方法分析了 2019 年 11 月至 2020 年 6 月期间在意大利发布的与疫苗相关的随机推文样本,目的是了解在大流行的第一阶段,与大流行前几个月相比,关于疫苗的 Twitter 对话如何发生变化。多学科团队从疫苗种类、疫苗立场、语气、目标人群、提到的信息来源等方面对推文进行了分析。多元对应分析用于识别与疫苗立场相关的变量。我们分析了 2473 条推文。58.2%提到了 COVID-19 疫苗。大多数持否定立场(38.1%),其次是推广性立场(32.5%)、中立立场(22%)和模糊立场(2.5%)。在大流行之前,否定立场的代表最多(69.6%)。在 2020 年 2 月和 3 月,否定性推文急剧减少,推广性和中立性推文主导了对话。在 2020 年 4 月至 6 月期间,推广性推文仍然更为突出(36.5%),其次是否定性推文(30%)和中立性推文(24.3%)。推文的语气主要是争论/抱怨,无论是推广性还是否定性推文。多元对应分析确定了否定性和中立性推文与推广性和模糊性推文相比的明确特征。总之,SARS-CoV-2 的出现导致意大利关于 Twitter 疫苗接种的讨论发生了深刻变化,推广性和模糊性推文的数量有所增加。对 Twitter 和其他社交媒体的系统监测,理想情况下与传统调查相结合,将使我们能够更好地了解意大利的疫苗犹豫情绪,并制定有针对性的、基于数据的沟通策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bdd/9157769/65881773a5cb/fpubh-10-824465-g0004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bdd/9157769/1f137d760de3/fpubh-10-824465-g0002.jpg
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COVID-19 Vaccine Hesitancy: Analysing Twitter to Identify Barriers to Vaccination in a Low Uptake Region of the UK.新冠疫苗犹豫:分析推特以识别英国低接种率地区的疫苗接种障碍
Front Digit Health. 2022 Jan 24;3:804855. doi: 10.3389/fdgth.2021.804855. eCollection 2021.
3
Characterizing polarization in online vaccine discourse-A large-scale study.
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4
Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy.基于词典的情感分析来检测意大利推特上对 COVID-19 疫苗的意见和态度。
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