Making a Case for Social Media Corpus for Detecting Depression
This addresses the challenge of noisy, non-standard language for mental health detection, but is incremental as it applies existing NLP methods to a new dataset.
The researchers tackled the problem of detecting depression by building a corpus from social media data, showing a high correlation with standard depression corpora.
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.