Table_2_Depressive Emotion Detection and Behavior Analysis of Men Who Have Sex With Men via Social Media.xlsx

BackgroundA large amount of evidence has indicated an association between depression and HIV risk among men who have sex with men (MSM), but traditional questionnaire-based methods are limited in timely monitoring depressive emotions with large sample sizes. With the development of social media and machine learning techniques, MSM depression can be well monitored in an online and easy-to-use manner. Thereby, we adopt a machine learning algorithm for MSM depressive emotion detection and behavior analysis with online social networking data.
MethodsA large-scale MSM data set including 664,335 users and over 12 million posts was collected from the most popular MSM-oriented geosocial networking mobile application named Blued. Also, a non-MSM Benchmark data set from Twitter was used. After data preprocessing and feature extraction of these two data sets, a machine learning algorithm named XGBoost was adopted for detecting depressive emotions.
ResultsThe algorithm shows good performance in the Blued and Twitter data sets. And three extracted features significantly affecting the depressive emotion detection were found, including depressive words, LDA topic words, and post-time distribution. On the one hand, the MSM with depressive emotions published posts with more depressive words, negative words and positive words than the MSM without depressive emotions. On the other hand, in comparison with the non-MSM with depressive emotions, the MSM with depressive emotions showed more significant depressive symptoms, such as insomnia, depressive mood, and suicidal thoughts.
ConclusionsThe online MSM depressive emotion detection using machine learning can provide a proper and easy-to-use way in real-world applications, which help identify high-risk individuals at the early stage of depression for further diagnosis.

研究背景
大量研究证据表明，男男性行为者（men who have sex with men, MSM）群体中抑郁症状与艾滋病病毒（HIV）感染风险存在显著关联，但传统基于问卷的监测方法难以实现大样本量下抑郁情绪的及时追踪。随着社交媒体与机器学习技术的发展，现已可通过线上便捷途径对男男性行为者的抑郁情绪开展监测。据此，本研究采用机器学习算法，结合在线社交网络数据进行男男性行为者抑郁情绪检测与行为分析。
研究方法
本研究从当前最具影响力的男男性行为者定向地理社交移动应用Blued中，采集得到包含664335名用户、逾1200万条发帖内容的大规模男男性行为者数据集；同时引入来自Twitter的非男男性行为者基准数据集。对上述两类数据集完成数据预处理与特征提取后，本研究采用XGBoost机器学习算法开展抑郁情绪检测任务。
研究结果
该算法在Blued与Twitter数据集上均展现出良好的分类性能。本研究共提取出三类对抑郁情绪检测具有显著影响的特征，分别为抑郁相关词汇、潜在狄利克雷分配（Latent Dirichlet Allocation, LDA）主题词汇以及发帖时间分布。一方面，相较于无抑郁情绪的男男性行为者，存在抑郁情绪的该群体发布的帖子中包含更多抑郁相关词汇、负面词汇与正面词汇；另一方面，与存在抑郁情绪的非男男性行为者相比，存在抑郁情绪的男男性行为者表现出更为显著的抑郁症状，例如失眠、抑郁心境与自杀意念。
研究结论
采用机器学习技术开展线上男男性行为者抑郁情绪检测，可为实际应用场景提供一种便捷高效的监测手段，有助于在抑郁早期阶段识别高风险个体，以便开展进一步的诊断干预。