@文章{信息:doi/10.2196/39324，作者=“Lorenzo- luaces, Lorenzo和Howard, Jacqueline和Edinger, Andy和Yan, Harry Yaojun和Rutter, Lauren A和Valdez, Danny和Bollen, Johan”，标题=“社会人口统计学和社会心理健康症状的转化诊断(内化症状和语言的在线队列研究)I和II:横断面调查与底部分析”，期刊=“JMIR Form Res”，年份=“2022”，月份=“10月”，日=“20”，卷=“6”，数=“10”，页数=“e39324”，关键词=“抑郁症;焦虑;疼痛;酒精;背景:内在化、外在化和躯体形式障碍是最常见和致残的精神病理学形式。我们对这些临床问题的理解由于依赖于自我报告和使用小样本的研究而受到限制。社交媒体已经成为一个令人兴奋的渠道，可以从个人身上收集大量纵向数据样本来研究精神病理学。目的:本研究报告了两项正在进行的大型研究的结果，我们从Twitter和自我报告的临床筛查量表中收集了数据，即在线队列内化症状和语言(SOCIAL)研究I和II。方法:参与者是使用Twitter的成年人样本(SOCIAL I: N=1123)，目标是在年龄、出生性别、种族和民族方面具有全国代表性，以及中西部的大学生样本(SOCIAL II: N=1988)，其中61.78(1228/1988)是Twitter用户。 For all participants who were Twitter users, we asked for access to their Twitter handle, which we analyzed using Botometer, which rates the likelihood of an account belonging to a bot. We divided participants into 4 groups: Twitter users who did not give us their handle or gave us invalid handles (invalid), those who denied being Twitter users (no Twitter, only available for SOCIAL II), Twitter users who gave their handles but whose accounts had high bot scores (bot-like), and Twitter users who provided their handles and had low bot scores (valid). We explored whether there were significant differences among these groups in terms of their sociodemographic features, clinical symptoms, and aspects of social media use (ie, platforms used and time). Results: In SOCIAL I, most individuals were classified as valid (580/1123, 51.65{\%}), and a few were deemed bot-like (190/1123, 16.91{\%}). A total of 31.43{\%} (353/1123) gave no handle or gave an invalid handle (eg, entered ``N/A''). In SOCIAL II, many individuals were not Twitter users (760/1988, 38.23{\%}). Of the Twitter users in SOCIAL II (1228/1988, 61.78{\%}), most were classified as either invalid (515/1228, 41.94{\%}) or valid (484/1228, 39.41{\%}), with a smaller fraction deemed bot-like (229/1228, 18.65{\%}). Participants reported high rates of mental health diagnoses as well as high levels of symptoms, especially in SOCIAL II. In general, the differences between individuals who provided or did not provide their social media handles were small and not statistically significant. Conclusions: Triangulating passively acquired social media data and self-reported questionnaires offers new possibilities for large-scale assessment and evaluation of vulnerability to mental disorders. The propensity of participants to share social media handles is likely not a source of sample bias in subsequent social media analytics. ", issn="2561-326X", doi="10.2196/39324", url="https://formative.www.mybigtv.com/2022/10/e39324", url="https://doi.org/10.2196/39324", url="http://www.ncbi.nlm.nih.gov/pubmed/36264616" }