Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early detection of stress and depression. Although many research works have been introduced targeting the early recognition of stress and depression, there are still limitations. There have been proposed multi-task learning settings, which use depression and emotion (or figurative language) as the primary and auxiliary tasks respectively. However, although stress is inextricably linked with depression, researchers face these two tasks as two separate tasks. To address these limitations, we present the first study, which exploits two different datasets collected under different conditions, and introduce two multitask learning frameworks, which use depression and stress as the main and auxiliary tasks respectively. Specifically, we use a depression dataset and a stressful dataset including stressful posts from ten subreddits of five domains. In terms of the first approach, each post passes through a shared BERT layer, which is updated by both tasks. Next, two separate BERT encoder layers are exploited, which are updated by each task separately. Regarding the second approach, it consists of shared and task-specific layers weighted by attention fusion networks. We conduct a series of experiments and compare our approaches with existing research initiatives, single-task learning, and transfer learning. Experiments show multiple advantages of our approaches over state-of-the-art ones.

翻译：现代生活节奏加快，压力与抑郁在各年龄段人群中普遍存在。人们通过社交媒体表达情感，因此社交媒体成为早期检测压力与抑郁的重要信息来源。尽管已有诸多研究致力于压力与抑郁的早期识别，但仍存在局限性。现有研究提出多任务学习框架，将抑郁和情绪（或比喻性语言）分别作为主任务和辅助任务。然而，尽管压力与抑郁密不可分，研究者仍将二者视为独立任务。为突破这一局限，我们首次利用两种不同条件下采集的数据集，提出两种以抑郁和压力分别作为主任务和辅助任务的多任务学习框架。具体而言，我们使用一个抑郁数据集和一个压力数据集（包含来自五个领域十个子版块的压力相关帖子）。第一种方法中，每条帖子经过共享的BERT层（由两个任务共同更新），再分别通过两个独立的BERT编码层（由各自任务单独更新）。第二种方法则包含由注意力融合网络加权的共享层与任务特定层。通过系列实验，我们将所提方法与现有研究、单任务学习及迁移学习进行对比。实验表明，我们的方法在性能上显著优于当前最先进技术。