Use of Health Belief Model-Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study
Use of Health Belief Model-Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study
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DOI：
10.2196/20493
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发表时间：
2020-07-01
影响因子：
8.5
通讯作者：
Wee, Hwee Lin
中科院分区：
文献类型：
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作者：
Raamkumar, Aravind Sesagiri;Tan, Soon Guan;Wee, Hwee Lin
背景资料：公共卫生当局一直建议采取物理距离和口罩等干预措施，以减少冠状病毒病（COVID-19）在社区内的传播。应确定公众对此类干预措施的看法，以使公共卫生当局能够有效地解决合理的问题。健康信念模型（Health Belief Model，HBM）已被用于描述以往疫情期间来自社交媒体的用户生成内容，旨在了解公众的健康行为。目的：本研究旨在开发和评估基于深度学习的文本分类模型，用于对COVID-19疫情期间发布的社交媒体内容进行分类，使用HBM的四个关键结构。我们将特别关注与公共卫生当局提出的物理距离干预措施有关的内容。方法：本研究的数据集是通过分析公众在Facebook上发布的评论来准备的，这些评论是针对新加坡卫生部（MOH）、疾病控制和预防中心（Centers for Disease Control and Prevention）和英国公共卫生部（Public Health England）这三个公共卫生机构的COVID-19相关帖子而发布的。在物理距离的背景下所做的评论被手动分类为四个HBM结构中的每一个：感知严重性，感知敏感性，感知障碍和感知利益。使用16，752条评论的精选数据集，对基于门控递归单元的递归神经网络模型进行了训练和验证，用于文本分类。准确性和二进制交叉熵损失被用来评估模型。特异性，敏感性和平衡的准确性被用来评估的分类结果在MOH case study.Results：HBM文本分类模型实现的平均准确率为0.92，0.95，0.91，和0.94的结构感知的敏感性，感知的严重性，感知的好处，和感知的障碍，分别。在MOH Facebook评论的案例研究中，所有HBM构建体的特异性均高于96%。敏感性分别为94.3%和90.9%，感知的严重性和感知的好处。此外，敏感性为79.6%和81.5%的知觉易感性和知觉障碍，分别。分类模型能够准确地预测趋势的流行结构的时间段内检查的casestudy.Conclusions：在这项研究中开发的基于深度学习的文本分类器有助于确定公众对物理距离的看法，使用HBM的四个关键结构。卫生官员可以利用分类模型，通过社交媒体的透镜来描述公众的健康行为。在未来的研究中，我们打算扩展该模型，以研究公众对公共卫生当局其他重要干预措施的看法。
Background: Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public.Objective: This study is aimed at developing and evaluating deep learning-based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study.Methods: The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19-related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit-based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study.Results: The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study.Conclusions: The deep learning-based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.