Instagram photos reveal predictive markers of depression
Instagram photos reveal predictive markers of depression
复制标题
DOI：
10.1140/epjds/s13688-017-0110-z
复制
发表时间：
2017-08-08
期刊：
影响因子：
3.6
通讯作者：
Danforth, Christopher M.
中科院分区：
文献类型：
--
作者：
Reece, Andrew G.;Danforth, Christopher M.
使用来自166名个人的Instagram数据，我们应用机器学习工具成功识别抑郁症的标志物。统计特征是从43,950张参与者的Instagram照片中计算提取的，使用颜色分析，元数据组件和算法人脸检测。由此产生的模型优于全科医生的平均抑郁症无辅助诊断成功率。即使分析仅限于抑郁症患者首次被诊断之前发表的帖子，这些结果也是成立的。照片属性的人类评级（快乐，悲伤等）抑郁症的预测因子较弱，与计算机生成的特征无关。这些结果为早期筛查和发现精神疾病提供了新的途径。
Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners' average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.