杂志文章%@ 2564-1891 %I JMIR出版物%V 2 %N 卡塔尔世界杯8强波胆分析1 %P 37077 %T推特上支持/抗covid -19疫苗信息的传播与特征之间的关联:精化似可模型的应用%A Saini,Vipin %梁阿，李林%杨阿，雨辰%阿乐，香麦%吴阿，春英%+国立阳明交通大学医学院公共卫生研究所，台北市北头区荔农街二段155号，邮编:112,886 228267000 ext 67156，liang.lilin@nycu.edu.tw %K COVID-19 %K推特%K提供疫苗%K抗疫苗%K阐述可能性模型%K信息流行病学%K传播%K内容分析%K情感效价%K社交媒体%D 2022 %7 27.6.2022 %9原创论文%J JMIR信息流行病学%G英语%X背景:微博网站上关于一个人对接种疫苗的立场的信息可能会影响读者是否接受疫苗的决定。理解社交媒体上与COVID-19有关的预防接种和反疫苗信息的传播至关重要;然而，这方面的研究仍然有限。目的:本研究应用精化似然模型(ELM)探讨可能吸引推特用户的疫苗立场信息特征。首先，我们研究了疫苗立场推文的特征与转发的可能性和数量之间的关联。其次，我们确定了在分享信息的决策过程中，中心路线和外围路线的相对重要性。方法:分析了2021年4月26日至8月26日期间来自美国的含有provvaccine和antivaccine话题标签(N=150,338)的英文推文。采用Logistic和广义负二项回归预测转发结果。 The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results: Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions: The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics. %M 35783451 %R 10.2196/37077 %U https://infodemiology.www.mybigtv.com/2022/1/e37077 %U https://doi.org/10.2196/37077 %U http://www.ncbi.nlm.nih.gov/pubmed/35783451