@文章{信息:doi/10.2196/37861，作者=“Ke, Si Yang和neley - tass, E Shannon和Barnes, Michael和Hanson, Carl L和girau - carrier, Christophe和Snell, Quinn”，标题=“关于佩戴口罩和接种疫苗的COVID-19健康信念:深度学习方法”，期刊=“JMIR信息流行病学”，年=“2022”，月=“10月”，日=“31”，卷=“2”，数=“2”，页=“e37861”，关键词=“COVID-19;健康信念模型;深度学习;面具;疫苗接种;机器学习;疫苗数据集;推特;内容分析;infodemic; infodemiology; misinformation; health belief", abstract="Background: Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19--related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19. Objective: The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action. Methods: A total of 646,885,238 COVID-19--related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets. Results: In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81{\%} classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action. Conclusions: During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals. ", issn="2564-1891", doi="10.2196/37861", url="https://infodemiology.www.mybigtv.com/2022/2/e37861", url="https://doi.org/10.2196/37861", url="http://www.ncbi.nlm.nih.gov/pubmed/36348979" }