Characterizing discourses about COVID-19 vaccines on Twitter: a topic modeling and sentiment analysis approach

悲伤 厌恶 健康传播 惊喜 情绪分析 接种疫苗 愤怒 社会化媒体 病毒学 医学 计算机科学 心理学 社会心理学 公共关系 政治学 机器学习 法学
作者
Yuan Wang,Yonghao Chen
出处
期刊:Journal of communication in healthcare [Informa]
卷期号:16 (1): 103-112 被引量:2
标识
DOI:10.1080/17538068.2022.2054196
摘要

Background Evidence-based health communication is crucial for facilitating vaccine-related knowledge and addressing vaccine hesitancy. To that end, it is important to understand the discourses about COVID-19 vaccination and attend to the publics’ emotions underlying those discourses.Methods We collect tweets related to COVID-19 vaccines from March 2020 to March 2021. In total, 304,292 tweets from 134,015 users are collected. We conduct a Latent Dirichlet Allocation (LDA) modeling analysis and a sentiment analysis to analyze the discourse themes and sentiments.Results This study identifies seven themes of COVID-19 vaccine-related discourses. Vaccine advocacy (24.82%) is the most widely discussed topic about COVID-19 vaccines, followed by vaccine hesitancy (22.29%), vaccine rollout (12.99%), vaccine facts (12.61%), recognition for healthcare workers (12.47%), vaccine side effects (10.07%), and vaccine policies (4.75%). Trust is the most salient emotion associated with COVID-19 vaccine discourses, followed by anticipation, fear, joy, sadness, anger, surprise, and disgust. Among the seven topics, vaccine advocacy tweets are most likely to receive likes and comments, and vaccine fact tweets are most likely to receive retweets.Conclusions When talking about vaccines, publics’ emotions are dominated by trust and anticipation, yet mixed with fear and sadness. Although tweets about vaccine hesitancy are prevalent on Twitter, those messages receive fewer likes and comments than vaccine advocacy messages. Over time, tweets about vaccine advocacy and vaccine facts become more dominant whereas tweets about vaccine hesitancy become less dominant among COVID-19 vaccine discourses, suggesting that publics become more confident about COVID-19 vaccines as they obtain more information.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
grzzz完成签到,获得积分10
刚刚
Wcc完成签到,获得积分10
1秒前
1秒前
屈灿完成签到,获得积分20
1秒前
文艺冷荷完成签到,获得积分10
1秒前
美丽千易完成签到 ,获得积分10
1秒前
龍焱发布了新的文献求助10
2秒前
2秒前
大熊完成签到,获得积分10
2秒前
墨墨叻完成签到,获得积分10
4秒前
lww发布了新的文献求助30
5秒前
xzrch完成签到 ,获得积分10
5秒前
学术学习完成签到,获得积分10
6秒前
pan完成签到,获得积分10
6秒前
罗氏集团发布了新的文献求助10
7秒前
8秒前
8秒前
小杨完成签到 ,获得积分10
9秒前
MaQY完成签到,获得积分10
9秒前
汤飞飞发布了新的文献求助10
9秒前
虚幻采枫完成签到,获得积分10
10秒前
Jasper应助ddforever采纳,获得10
10秒前
爱科研的龙完成签到,获得积分10
12秒前
小全发布了新的文献求助10
12秒前
青岛完成签到,获得积分20
13秒前
凌感动完成签到,获得积分10
13秒前
小月Anna发布了新的文献求助100
13秒前
CodeCraft应助lihaifeng采纳,获得10
13秒前
研友_yLpzpZ完成签到,获得积分10
14秒前
个性的紫菜应助miaomiao采纳,获得10
14秒前
DrW完成签到,获得积分10
14秒前
curry发布了新的文献求助10
16秒前
大模型应助科研通管家采纳,获得10
16秒前
16秒前
顶刊上岸完成签到,获得积分10
18秒前
慕青应助shawn采纳,获得10
18秒前
思源应助WX采纳,获得10
19秒前
........完成签到 ,获得积分10
19秒前
等待蚂蚁完成签到,获得积分10
19秒前
panpan完成签到,获得积分10
20秒前
高分求助中
The three stars each : the Astrolabes and related texts 1070
Manual of Clinical Microbiology, 4 Volume Set (ASM Books) 13th Edition 1000
Sport in der Antike 800
De arte gymnastica. The art of gymnastics 600
少脉山油柑叶的化学成分研究 530
Sport in der Antike Hardcover – March 1, 2015 500
Boris Pesce - Gli impiegati della Fiat dal 1955 al 1999 un percorso nella memoria 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2407694
求助须知:如何正确求助?哪些是违规求助? 2104332
关于积分的说明 5311730
捐赠科研通 1831920
什么是DOI,文献DOI怎么找? 912791
版权声明 560691
科研通“疑难数据库(出版商)”最低求助积分说明 488060