Depression is debilitating, and not uncommon. Indeed, studies of excessive
social media users show correlations with depression, ADHD, and other mental
health concerns. Given that there is a large number of people with excessive
social media usage, then there is a significant population of potentially
undiagnosed users and posts that they create. In this paper, we propose a
depression severity detection system using a semi-supervised learning technique
to predict if a post is from a user who is experiencing severe, moderate, or
low (non-diagnostic) levels of depression. Namely, we use a trained model to
classify a large number of unlabelled social media posts from Reddit, then use
these generated labels to train a more powerful classifier. We demonstrate our
framework on Detecting Signs of Depression from Social Media Text -
LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.

使用半监督学习技术提出了一种检测抑郁症严重程度的系统，通过预测帖子是否来自经历严重、中度或低（非诊断性）抑郁水平的用户，使用 Reddit 上的大量未标记社交媒体帖子进行训练模型的分类。在 LT-EDI@RANLP 2023 共享任务中，我们的框架排名第三。