  The social media platform provides an opportunity to gain valuable insights
into user behaviour. Users mimic their internal feelings and emotions in a
disinhibited fashion using natural language. Techniques in Natural Language
Processing have helped researchers decipher standard documents and cull
together inferences from massive amount of data. A representative corpus is a
prerequisite for NLP and one of the challenges we face today is the
non-standard and noisy language that exists on the internet. Our work focuses
on building a corpus from social media that is focused on detecting mental
illness. We use depression as a case study and demonstrate the effectiveness of
using such a corpus for helping practitioners detect such cases. Our results
show a high correlation between our Social Media Corpus and the standard corpus
for depression.
