COVID-19: Detecting Depression Signals during Stay-At-Home Period

TL;DR

This paper investigates social media data during COVID-19 stay-at-home orders to detect depression signals using topic modeling and psycholinguistic features, achieving high classification accuracy and highlighting mental health challenges.

Contribution

It introduces a novel approach combining topic modeling and psycholinguistic attributes to detect depression signals in social media during the pandemic.

Findings

Best classifier achieved F-1 score of 0.8

Significant improvement over baseline by 0.173

Identified potential growth of depression signals over time

Abstract

The new coronavirus outbreak has been officially declared a global pandemic by the World Health Organization. To grapple with the rapid spread of this ongoing pandemic, most countries have banned indoor and outdoor gatherings and ordered their residents to stay home. Given the developing situation with coronavirus, mental health is an important challenge in our society today. In this paper, we discuss the investigation of social media postings to detect signals relevant to depression. To this end, we utilize topic modeling features and a collection of psycholinguistic and mental-well-being attributes to develop statistical models to characterize and facilitate representation of the more subtle aspects of depression. Furthermore, we predict whether signals relevant to depression are likely to grow significantly as time moves forward. Our best classifier yields F-1 scores as high as 0.8 and surpasses the utilized baseline by a considerable margin, 0.173. In closing, we propose several future research avenues.