NarrationDep: Narratives on Social Media For Automatic Depression Detection
This work addresses the challenge of modeling user narratives from social media for depression detection, which could aid in mental health monitoring, but it appears incremental as it builds on existing deep learning approaches for text analysis.
The paper tackles the problem of automatically detecting depression from social media posts by developing NarrationDep, a deep learning model that analyzes user tweets to identify depression-associated narratives, and reports that it outperforms other models on various datasets.
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.