Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms

TL;DR

This paper explores how mood patterns in social media posts can improve the detection of depressive symptoms, addressing limitations of proxy signals used in current models.

Contribution

It introduces a novel approach to depression detection by constructing mood profiles, grounded in affective disorder symptomatology, to enhance model validity.

Findings

Mood patterns correlate with depressive symptoms

Mood profiles improve depression detection accuracy

Proposed method aligns with clinical symptomatology

Abstract

Depression is the leading cause of disability worldwide. Initial efforts to detect depression signals from social media posts have shown promising results. Given the high internal validity, results from such analyses are potentially beneficial to clinical judgment. The existing models for automatic detection of depressive symptoms learn proxy diagnostic signals from social media data, such as help-seeking behavior for mental health or medication names. However, in reality, individuals with depression typically experience depressed mood, loss of pleasure nearly in all the activities, feeling of worthlessness or guilt, and diminished ability to think. Therefore, a lot of the proxy signals used in these models lack the theoretical underpinnings for depressive symptoms. It is also reported that social media posts from many patients in the clinical setting do not contain these signals. Based on this research gap, we propose to monitor a type of signal that is well-established as a class of symptoms in affective disorders -- mood. The mood is an experience of feeling that can last for hours, days, or even weeks. In this work, we attempt to enrich current technology for detecting symptoms of potential depression by constructing a 'mood profile' for social media users.