Identifying Depression on Twitter

TL;DR

This paper presents a novel text classification approach using Twitter data to predict Major Depressive Disorder with 81% accuracy, aiding early diagnosis and mental health intervention.

Contribution

It introduces a new methodology treating depression prediction as a text classification problem using social media posts, achieving high accuracy with a large tweet corpus.

Findings

81% classification accuracy

Precision score of 0.86

Potential tool for early depression detection

Abstract

Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a crowdsourced method to compile a list of Twitter users who profess to being diagnosed with depression. Using up to a year of prior social media postings, we utilize a Bag of Words approach to quantify each tweet. Lastly, we leverage several statistical classifiers to provide estimates to the risk of depression. Our work posits a new methodology for constructing our classifier by treating social as a text-classification problem, rather than a behavioral one on social media platforms. By using a corpus of 2.5M tweets, we achieved an 81% accuracy rate in classification, with a precision score of .86. We believe that this method may be helpful in developing tools that estimate the risk of an individual being depressed, can be employed by physicians, concerned individuals, and healthcare agencies to aid in diagnosis, even possibly enabling those suffering from depression to be more proactive about recovering from their mental health.