Privacy Aware Question-Answering System for Online Mental Health Risk
  Assessment
Prateek Chhikara Ujjwal Pasupulety John Marshall Dhiraj Chaurasia Shweta Kumari
Social media platforms have enabled individuals suffering from mental
illnesses to share their lived experiences and find the online support
necessary to cope. However, many users fail to receive genuine clinical
support, thus exacerbating their symptoms. Screening users based on what they
post online can aid providers in administering targeted healthcare and minimize
false positives. Pre-trained Language Models (LMs) can assess users' social
media data and classify them in terms of their mental health risk. We propose a
Question-Answering (QA) approach to assess mental health risk using the
Unified-QA model on two large mental health datasets. To protect user data, we
extend Unified-QA by anonymizing the model training process using differential
privacy. Our results demonstrate the effectiveness of modeling risk assessment
as a QA task, specifically for mental health use cases. Furthermore, the
model's performance decreases by less than 1% with the inclusion of
differential privacy. The proposed system's performance is indicative of a
promising research direction that will lead to the development of privacy-aware
diagnostic systems.