TY - JOUR AU - Benis, Arriel AU - Chatsubi, Anat AU - Levner, Eugene AU - Ashkenazi, Shai PY - 2021 DA - 201/10/14 TI - COVID-19大流行期间推特上关于流感、疫苗和疫苗接种的话题变化:基于人工智能的Infodemiology研究乔——JMIR Infodemiology SP - e31983六世- 1 - 1千瓦流感KW -千瓦的疫苗接种KW -社会媒体KW -社交网络KW -健康传播KW -人工智能KW -机器学习KW -文本挖掘KW - Infodemiology KW - COVID-19 KW - SARS-CoV-2 AB -背景:健康问题在社交媒体上的讨论是反映现实世界的重要信息来源对事件的响应和观点。它们在公共卫生保健中往往很重要，因为它们影响着影响犹豫不决的个人接种疫苗决策的途径。基于互联网搜索引擎查询的人工智能方法已被建议用于检测疾病爆发和人群行为。在社交媒体中，推特是搜索和分享关于卫生保健问题的意见和(错误)信息的常用平台，包括疫苗接种和疫苗。目的:我们的主要目标是支持设计和实施未来的电子卫生战略和社交媒体干预措施，以提高有针对性的传播运动的质量，从而提高流感疫苗接种率。我们的目标是定义一种基于人工智能的方法，以阐明推特上关于流感疫苗接种的帖子在COVID-19大流行期间是如何变化的。这些发现可支持适当的疫苗接种运动，并可推广到其他与健康有关的大众传播。方法:研究分为以下5个阶段:(1)收集美国Twitter上有关流感、疫苗和疫苗接种的推文;(2)使用机器学习技术进行数据清理和存储; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results: We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions: This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process. SN - 2564-1891 UR - https://infodemiology.www.mybigtv.com/2021/1/e31983 UR - https://doi.org/10.2196/31983 UR - http://www.ncbi.nlm.nih.gov/pubmed/34693212 DO - 10.2196/31983 ID - info:doi/10.2196/31983 ER -