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.

翻译：社交媒体帖子为理解用户的叙事及其意图提供了宝贵洞见，包括为自动建模社交媒体用户是否抑郁提供了可能。挑战在于如何从其在线社交媒体帖子中忠实建模用户叙事，这在多种不同应用中均具有潜在价值。我们开发了一种新颖且有效的模型——\texttt{NarrationDep}，该模型专注于检测与抑郁相关的叙事。通过分析用户的推文，\texttt{NarrationDep} 能够准确识别关键叙事。\texttt{NarrationDep} 是一个深度学习框架，可联合建模个体用户推文表征与用户推文聚类。因此，\texttt{NarrationDep} 采用了一种新颖的双层深度学习模型结构：第一层基于社交媒体文本帖子进行建模，第二层学习与聚类相关联的推文语义表征。为忠实建模这些聚类表征，第二层引入了一个新颖组件，能够从用户帖子中进行分层学习。实验结果表明，我们的框架在多种数据集上均优于包括近期开发模型在内的其他对比模型。