As the impact of technology on our lives is increasing, we witness increased use of social media that became an essential tool not only for communication but also for sharing information with community about our thoughts and feelings. This can be observed also for people with mental health disorders such as depression where they use social media for expressing their thoughts and asking for help. This opens a possibility to automatically process social media posts and detect signs of depression. We build several large pre-trained language model based classifiers for depression detection from social media posts. Besides fine-tuning BERT, RoBERTA, BERTweet, and mentalBERT were also construct two types of ensembles. We analyze the performance of our models on two data sets of posts from social platforms Reddit and Twitter, and investigate also the performance of transfer learning across the two data sets. The results show that transformer ensembles improve over the single transformer-based classifiers.

翻译：随着技术对我们生活的影响日益加深，我们见证了社交媒体的广泛应用，它已成为不仅用于交流，还用于与社区分享思想和情感的重要工具。这一点同样体现在抑郁症等心理健康障碍患者身上，他们通过社交媒体表达想法并寻求帮助。这为自动处理社交媒体帖子并检测抑郁迹象提供了可能性。我们基于多个大型预训练语言模型构建了分类器，用于从社交媒体帖子中检测抑郁症。除了微调BERT、RoBERTa、BERTweet和mentalBERT外，我们还构建了两种类型的集成模型。我们分析了模型在来自社交平台Reddit和Twitter的两个数据集上的表现，并研究了跨这两个数据集的迁移学习性能。结果表明，基于变压器的集成模型优于单一的基于变压器的分类器。