Making a Case for Social Media Corpus for Detecting Depression

Adil Rajput; Samara Ahmed

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arXiv · 1902.00702

Making a Case for Social Media Corpus for Detecting Depression

Adil Rajput·Samara Ahmed

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Abstract

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.

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Adil Rajput, Samara Ahmed. 2019-02-02. Making a Case for Social Media Corpus for Detecting Depression. https://arxiv.org/abs/1902.00702

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