  Given the current state of the world, because of existing situations around
the world, millions of people suffering from mental illnesses feel isolated and
unable to receive help in person. Psychological studies have shown that our
state of mind can manifest itself in the linguistic features we use to
communicate. People have increasingly turned to online platforms to express
themselves and seek help with their conditions. Deep learning methods have been
commonly used to identify and analyze mental health conditions from various
sources of information, including social media. Still, they face challenges,
including a lack of reliability and overconfidence in predictions resulting in
the poor calibration of the models. To solve these issues, We propose UATTA-EB:
Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing
reliable and well-calibrated predictions to classify six possible types of
mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD
by analyzing unstructured user data on Reddit.
