Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of $150k$ items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adapt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.

本文设计并评估了一种捕捉在社交媒体上关于CVD相关的心理健康症状如何不同地表达男女之间的方法，并采用GeM，一种任务自适应的多任务学习方法来区分不同性别的语言中的心理健康症状，GeM模型相比于当前最先进的语言模型，能更好地识别和预测心理健康问题和性别标签。

一种从 Reddit 理解心理健康的计算方法：基于知识的多任务学习框架