TY - JOUR AU - Ricard, Benjamin J AU - Marsch, Lisa A AU - Crosier, Benjamin AU - Hassanpour, Saeed PY - 2018 DA - 2018/12/06 TI -探索社区生成的社交媒体内容用于检测抑郁症的效用:Instagram上的分析研究JO - J Med Internet Res SP - e11817 VL - 20 IS - 12 KW -机器学习KW -抑郁症KW -社交媒体KW -心理健康AB -背景:个人在各种社交媒体平台上发布的内容已成功用于识别包括抑郁症在内的精神疾病。然而，这一领域之前的大部分工作都集中在用户生成的内容上，即由个人创建的内容，例如个人的帖子和图片。在这项研究中，我们探索了社区生成内容的预测能力，即由朋友或追随者社区生成的数据，而不是由单个人生成的数据，以识别社交媒体用户中的抑郁症。目的:本研究的目的是评估社交媒体上社区生成内容的效用，如对个人帖子的评论，以预测临床验证的患者健康问卷-8 (PHQ-8)评估问卷所定义的抑郁症。我们假设这项研究的结果可能为下一代人群水平的精神疾病风险评估和干预提供新的见解。方法:我们在众包平台上创建了一项基于网络的调查，参与者可以访问他们的Instagram档案，并提供他们对PHQ-8的回答，作为抑郁症状态的参考标准。在数据质量保证和后处理后，本研究分析了749名参与者的数据。为了建立我们的预测模型，我们从Instagram的帖子标题和评论中提取了语言特征，包括多个情绪得分、表情符号情绪分析结果以及诸如点赞数和平均评论长度等元变量。在本研究中，10.4%(78/749)的数据被作为测试集。 The remaining 89.6% (671/749) of the data were used to train an elastic-net regularized linear regression model to predict PHQ-8 scores. We compared different versions of this model (ie, a model trained on only user-generated data, a model trained on only community-generated data, and a model trained on the combination of both types of data) on a test set to explore the utility of community-generated data in our predictive analysis. Results: The 2 models, the first trained on only community-generated data (area under curve [AUC]=0.71) and the second trained on a combination of user-generated and community-generated data (AUC=0.72), had statistically significant performances for predicting depression based on the Mann-Whitney U test (P=.03 and P=.02, respectively). The model trained on only user-generated data (AUC=0.63; P=.11) did not achieve statistically significant results. The coefficients of the models revealed that our combined data classifier effectively amalgamated both user-generated and community-generated data and that the 2 feature sets were complementary and contained nonoverlapping information in our predictive analysis. Conclusions: The results presented in this study indicate that leveraging community-generated data from social media, in addition to user-generated data, can be informative for predicting depression among social media users. SN - 1438-8871 UR - //www.mybigtv.com/2018/12/e11817/ UR - https://doi.org/10.2196/11817 UR - http://www.ncbi.nlm.nih.gov/pubmed/30522991 DO - 10.2196/11817 ID - info:doi/10.2196/11817 ER -