Forecasting the onset and course of mental illness with Twitter data.
Forecasting the onset and course of mental illness with Twitter data.
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DOI：
10.1038/s41598-017-12961-9
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发表时间：
2017-10-11
影响因子：
4.6
通讯作者：
Langer EJ
中科院分区：
文献类型：
--
作者：
Reece AG;Reagan AJ;Lix KLM;Dodds PS;Danforth CM;Langer EJ
我们开发了计算模型来预测Twitter用户中抑郁症和创伤后应激障碍的出现。研究人员收集了204人的Twitter数据和抑郁症病史细节（105人患有抑郁症，99人健康）。我们从参与者的推文（N = 279,951）中提取了衡量影响、语言风格和上下文的预测特征，并利用这些特征与监督学习算法建立了模型。由此产生的模型成功地区分了抑郁和健康的内容，并且在诊断抑郁症方面比全科医生的平均成功率要高，尽管是在不同的人群中。即使分析仅限于第一次抑郁症诊断之前发布的内容，结果也成立。状态-空间-时间分析表明，抑郁症的发作可以在诊断前几个月从Twitter数据中检测到。预测结果在被诊断为PTSD的个体的单独样本中得到了重复（Nusers = 174, Ntweets = 243,775）。一个状态空间时间序列模型几乎在创伤后立即揭示了PTSD的指标，通常在临床诊断前几个月。这些方法为早期筛查和发现精神疾病提供了一种数据驱动的预测方法。
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 (Nusers = 174, Ntweets = 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.
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