The infinity vaccine war: linguistic regularities and audience engagement of vaccine debate on Twitter

被引:1
|
作者
Peng, Rachel X. [1 ]
Wang, Ryan Yang [1 ]
机构
[1] Rochester Inst Technol, Coll Liberal Arts, Sch Commun, Rochester, NY 14623 USA
关键词
Anti-vaccination; Sentiment analysis; Social media engagement; Topic modeling; Twitter; SOCIAL MEDIA; HPV; INSIGHTS; COVERAGE; INTERNET;
D O I
10.1108/OIR-03-2022-0186
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Purpose - As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro and anti-vaxxers' tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences' engagement.Design/methodology/approach - Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement.Findings - In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro-and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro-and anti-vaxxers separately. Among the pro-vaxxers' tweets, the topics of "Child protection" and "COVID-19 situation" are positively predicting audiences' engagement. For anti-vaxxers, the topics of "Supporting Trump," "Injured children," "COVID-19 situation," "Media propaganda" and "Community building" are more appealing to audiences.Originality/value - This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery.
引用
收藏
页码:84 / 104
页数:21
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