Department of Psychology, Harvard University, Cambridge, MA, 02138, USA.
Computational Story Lab, Vermont Advanced Computing Core, and the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401, USA.
Sci Rep. 2017 Oct 11;7(1):13006. doi: 10.1038/s41598-017-12961-9.
We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners' average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (N = 174, N = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.
我们开发了计算模型，以预测 Twitter 用户中抑郁和创伤后应激障碍的出现。从 204 名个体（105 名抑郁，99 名健康）中收集了 Twitter 数据和抑郁史细节。我们从参与者的推文（N=279951）中提取了衡量情感、语言风格和上下文的预测特征，并使用监督学习算法使用这些特征构建模型。结果模型成功地区分了抑郁和健康的内容，并且与一般医生诊断抑郁症的平均成功率相当，尽管是在另一个人群中。即使将分析仅限于首次诊断前发布的内容，结果仍然成立。状态空间时间分析表明，从 Twitter 数据中可能在诊断前几个月就能检测到抑郁症的发作。使用另一个被诊断患有创伤后应激障碍的个体样本（N=174，N=243775）复制了预测结果。状态空间时间序列模型揭示了创伤后几乎立即出现 PTSD 的指标，通常在临床诊断前数月。这些方法为早期筛查和检测精神疾病提供了一种数据驱动的预测方法。
