@文章{信息:doi/10.2196/26769，作者="张一鹏和吕，韩佳和刘，玉宝和张，西阳和王，于和罗，杰波"，标题="监测COVID-19大流行期间推特上的抑郁趋势:观察性研究"，期刊="JMIR信息流行病学"，年="2021"，月=" 7月"，日="18"，量="1"，数="1"，页="e26769"，关键词="心理健康;抑郁症;社交媒体;推特;数据挖掘;自然语言处理;《变形金刚》;背景:COVID-19大流行影响了人们的日常生活，并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。 Objective: This study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features' importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates. Methods: To study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. 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. ", issn="2564-1891", doi="10.2196/26769", url="https://infodemiology.www.mybigtv.com/2021/1/e26769", url="https://doi.org/10.2196/26769", url="http://www.ncbi.nlm.nih.gov/pubmed/34458682" }