Identifying Depression on Twitter
Moin Nadeem

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…
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Taxonomy
TopicsMental Health via Writing · Digital Mental Health Interventions · Sentiment Analysis and Opinion Mining
