Vaccine misinformation types and properties in Russian troll tweets.
Vaccine misinformation types and properties in Russian troll tweets.
复制标题
俄罗斯巨魔推文中的疫苗错误信息类型和属性。
DOI：
10.1016/j.vaccine.2021.12.040
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发表时间：
2022
期刊：
影响因子：
5.5
通讯作者：
Rains,StephenA
中科院分区：
文献类型：
--
作者：
Warner,EchoL;Barbati,JulianaL;Duncan,KaylinL;Yan,Kun;Rains,StephenA
目的了解俄罗斯troll在Twitter上发布的疫苗错误信息的内容和参与情况。方法对2020年从Twitter上获得的1959条troll推文进行疫苗错误信息编码（α = 0.77-0.97）。应用描述性、双变量和多变量负二项回归来估计疫苗错误信息与推文特征和参与度（即，结果关于人身危险（43.0%），侵犯公民自由（20.2%）和疫苗阴谋（18.6%）的错误信息是常见的。更多的错误信息推文使用反疫苗接种语言（97.3%对13.2%）和参考症状（37.4%对0.5%），而不是非错误信息推文。与非错误信息的推文相比，引用可靠来源的错误信息推文（14.0%对19.5%），被格式化为标题（39.2%对77.0%），并提到特定疫苗（11.3%对36.1%，所有p < 0.01）。个人危险性错误信息的转发率降低了83%（95%CI0.04 -0.66）.公民自由错误信息有显着较高的答复率（IRR：7.65，95%CI 1.06-55.46），但较低的整体参与度（IRR：0.38，95%CI 0.16-0.88）比非错误信息tweets.ConclusionsStrategies用于促进疫苗错误信息提供洞察疫苗错误信息的性质在线和公众的反应。我们的研究结果表明，有必要探讨用户是否拒绝或接受在线疫苗错误信息的影响。
ObjectiveTo identify the content of and engagement with vaccine misinformation from Russian trolls on Twitter.MethodsTroll tweets (N = 1959) obtained from Twitter in 2020 were coded for vaccine misinformation (α = 0.77–0.97). Descriptive, bivariate, and multivariable negative binomial regressions were applied to estimate robust incidence rate ratios (IRRs) and 95% confidence intervals (95 %CI) of vaccine misinformation associations with tweet characteristics and engagement (i.e., replies, likes, retweets).ResultsMisinformation about personal dangers (43.0%), civil liberty violations (20.2%), and vaccine conspiracies (18.6%) were common. More misinformation tweets used anti-vaccination language (97.3% vs. 13.2%) and referenced symptoms (37.4% vs. 0.5%) than non-misinformation tweets. Fewer misinformation tweets referenced credible sources (14.0% vs. 19.5%), were formatted as headlines (39.2% vs. 77.0%), and mentioned specific vaccines (11.3% vs. 36.1%, all p < 0.01) than non-misinformation tweets. Personal dangers misinformation had 83% lower rate of retweets (95 %CI 0.04–0.66). Civil liberties misinformation had significantly higher rate of replies (IRR: 7.65, 95 %CI 1.06–55.46), but lower overall engagement (IRR: 0.38, 95 %CI 0.16–0.88) than non-misinformation tweets.ConclusionsStrategies used to promote vaccine misinformation provide insight into the nature of vaccine misinformation online and public responses. Our findings suggest a need to explore influences on whether users reject or entertain online vaccine misinformation.