Table_1_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.

背景：众多证据表明，同性恋男性（MSM）中抑郁症与HIV风险之间存在关联，但传统的基于问卷的方法在及时监测大量样本的抑郁情绪方面存在局限性。随着社交媒体和机器学习技术的进步，MSM的抑郁情绪可以通过在线和易于使用的方式得到有效监控。因此，本研究采用机器学习算法，基于在线社交网络数据对MSM的抑郁情绪进行检测和行为分析。方法：从最受欢迎的MSM导向的地理社交网络移动应用程序“蓝领”中收集了包括664,335名用户和超过1200万篇帖子的大型MSM数据集。同时，还使用了来自Twitter的非MSM基准数据集。经过这两大数据集的数据预处理和特征提取后，采用了名为XGBoost的机器学习算法进行抑郁情绪的检测。结果：该算法在“蓝领”和Twitter数据集中表现出良好的性能。研究发现，有三个提取的特征对抑郁情绪检测有显著影响，包括抑郁词汇、LDA主题词汇和帖子时间分布。一方面，具有抑郁情绪的MSM发表的帖子中包含比无抑郁情绪的MSM更多的抑郁词汇、负面词汇和正面词汇。另一方面，与具有抑郁情绪的非MSM相比，具有抑郁情绪的MSM表现出更明显的抑郁症状，如失眠、抑郁情绪和自杀念头。结论：利用机器学习进行在线MSM抑郁情绪检测，可以提供一种在现实应用中恰当且易于使用的方法，有助于在抑郁症早期阶段识别高风险个体，以进行进一步的诊断。