%0期刊文章%@ 2564-1891 %I JMIR出版物%V 1% 卡塔尔世界杯8强波胆分析N 1% P e26769 %T监测COVID-19大流行期间推特上的抑郁趋势:观察性研究%A张一鹏%A吕汉佳%A刘玉宝%A张西阳%A王宇%A罗杰波%+罗切斯特大学，500 Joseph C Wilson Blvd, Rochester, NY, 1 585 276 5784, jluo@cs.rochester.edu %K心理健康%K抑郁症%K社交媒体%K推特%K数据挖掘%K自然语言处理%K变形金刚%K COVID-19 %D 2021 %7 18.7.2021 %9原创论文%J JMIR infodeology %G英文%X背景:新冠肺炎大流行影响了人们的日常生活，在全球范围内造成了经济损失。坊间证据表明，大流行增加了人们的抑郁水平。然而，在大流行期间缺乏抑郁症检测和监测的系统研究。目的:本研究旨在开发一种方法，以自动方式创建大规模抑郁症用户数据集，使该方法具有可扩展性，可以适应未来的事件;验证基于变压器的深度学习语言模型从日常语言中识别抑郁症患者的有效性;心理文本特征在抑郁症分类中的重要性研究最后，利用该模型监测疾病传播过程中不同人群抑郁水平的波动。方法:为了研究这一课题，我们设计了一种有效的基于正则表达式的搜索方法，并创建了最大的英文Twitter抑郁症数据集，其中包含2575个不同的抑郁症识别用户及其过去的推文。 To examine the effect of depression on people’s Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model’s tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information, and investigated these features’ relations to depression signals. Finally, we demonstrated our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results: Our fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states—New York, California, and Florida—shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions: This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19’s impact on people’s mental health. The noninvasive monitoring system can also be readily adapted to other big events besides COVID-19 and can be useful during future outbreaks. %M 34458682 %R 10.2196/26769 %U https://infodemiology.www.mybigtv.com/2021/1/e26769 %U https://doi.org/10.2196/26769 %U http://www.ncbi.nlm.nih.gov/pubmed/34458682