Ke Si Yang, Neeley-Tass E Shannon, Barnes Michael, Hanson Carl L, Giraud-Carrier Christophe, Snell Quinn
Department of Statistics Brigham Young University Provo, UT United States.
Department of Public Health Brigham Young University Provo, UT United States.
JMIR Infodemiology. 2022 Oct 31;2(2):e37861. doi: 10.2196/37861. eCollection 2022 Jul-Dec.
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.
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.
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.
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.
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.
在全球新冠疫情期间，一场全球信息疫情也随之出现，大量与新冠疫情相关的信息和错误信息通过社交媒体渠道传播。包括世界卫生组织（WHO）和疾病控制与预防中心（CDC）在内的各种组织以及其他知名人士发布了关于防止新冠疫情进一步传播的重要建议。
本研究的目的是利用机器学习和疫情期间的推特数据，探索关于佩戴口罩和疫苗接种的健康观念以及重要行动提示的影响。
总共筛选了6.46885238亿条与新冠疫情相关的英文推文，创建了一个佩戴口罩数据集和一个疫苗数据集。研究人员根据每条推文与健康信念模型（HBM）结构的相关性，对每个数据集的3500条推文的训练样本进行人工分类，并使用编码后的推文训练机器学习模型，以对数据集中的每条推文进行分类。
使用XLNet变压器模型针对与口罩相关和与疫苗相关的数据集总共训练了5个模型，每个模型的分类准确率至少达到81%。对于佩戴口罩和免疫接种，关于感知益处和障碍的健康观念最为明显；然而，这些观念的强度似乎因重要行动提示而有所不同。
在新冠疫情和信息疫情期间，通过大数据机器学习方法在推特上观察到的与感知益处和障碍相关的健康观念随时间变化，并因知名组织和个人发出的重要行动提示而有所不同。
