@文章{info:doi/10.2196/37077，作者=“Saini Vipin and Liang, Li-Lin and Yang, Yu-Chen and Le, Huong Mai and Wu Chun-Ying”，标题=“推特上支持/抗COVID-19疫苗信息传播与特征的关系:精化似然模型的应用”，期刊=“JMIR信息流行病学”，年=“2022”，月=“Jun”，日=“27”，卷=“2”，数=“1”，页数=“e37077”，关键词=“COVID-19;推特;provaccine;反对疫苗接种;详尽可能性模型;infodemiology;传播;内容分析;情感价;背景:微博网站上关于一个人对疫苗接种的立场的信息可能会影响读者是否要接种疫苗的决定。 Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited. Objective: This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods: English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. 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. ", issn="2564-1891", doi="10.2196/37077", url="https://infodemiology.www.mybigtv.com/2022/1/e37077", url="https://doi.org/10.2196/37077", url="http://www.ncbi.nlm.nih.gov/pubmed/35783451" }