  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.
