NarrationDep: Narratives on Social Media For Automatic Depression Detection

TL;DR

NarrationDep is a novel deep learning framework that automatically detects depression-related narratives in social media posts by jointly modeling individual tweets and user clusters, outperforming existing models.

Contribution

The paper introduces a two-layer deep learning model that hierarchically learns from social media posts to identify depression narratives, a novel approach in depression detection from social media.

Findings

Outperforms existing models on multiple datasets

Accurately identifies depression-related narratives

Effectively models user tweet clusters hierarchically

Abstract

Social media posts provide valuable insight into the narrative of users and their intentions, including providing an opportunity to automatically model whether a social media user is depressed or not. The challenge lies in faithfully modelling user narratives from their online social media posts, which could potentially be useful in several different applications. We have developed a novel and effective model called \texttt{NarrationDep}, which focuses on detecting narratives associated with depression. By analyzing a user's tweets, \texttt{NarrationDep} accurately identifies crucial narratives. \texttt{NarrationDep} is a deep learning framework that jointly models individual user tweet representations and clusters of users' tweets. As a result, \texttt{NarrationDep} is characterized by a novel two-layer deep learning model: the first layer models using social media text posts, and the second layer learns semantic representations of tweets associated with a cluster. To faithfully model these cluster representations, the second layer incorporates a novel component that hierarchically learns from users' posts. The results demonstrate that our framework outperforms other comparative models including recently developed models on a variety of datasets.