Predicting Depression and Anxiety on Reddit: a Multi-task Learning Approach
Predicting Depression and Anxiety on Reddit: a Multi-task Learning Approach
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DOI：
10.1109/asonam55673.2022.10068655
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Shailik Sarkar;Abdulaziz Alhamadani;Lulwah Alkulaib;Chang-Tien Lu
中科院分区：
文献类型：
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作者：
Shailik Sarkar;Abdulaziz Alhamadani;Lulwah Alkulaib;Chang-Tien Lu
心理健康危机最有力的指标之一是人们如何互动或表达自己。因此，社交媒体是提取有关用于表达个人感受的语言的用户级信息的理想来源。在美国日益严重的心理健康危机之后，必须分析人们的总体福祉，并调查如何利用他们的公共社交媒体帖子来检测不同的潜在心理健康状况。为此，我们提出一项研究，从“reddit”中收集与不同心理健康主题相关的帖子，以检测帖子的类型以及与帖子相关的心理健康问题的性质。检测心理健康相关问题的任务表明与帖子相关的心理健康状况。为了实现这一目标，我们开发了一个多任务学习模型，对于每个帖子，它利用单词和主题的潜在嵌入空间进行预测，并通过消息传递机制实现相关任务的信息共享。我们通过主动学习方法训练模型，以解决此特定任务缺乏标准化细粒度标签数据的问题。
One of the strongest indicators of a mental health crisis is how people interact with each other or express them-selves. Hence, social media is an ideal source to extract user-level information about the language used to express personal feelings. In the wake of the ever-increasing mental health crisis in the United States, it is imperative to analyze the general well-being of a population and investigate how their public social media posts can be used to detect different underlying mental health conditions. For that purpose, we propose a study that collects posts from “reddits” related to different mental health topics to detect the type of the post and the nature of the mental health issues that correlate to the post. The task of detecting mental health related issues indicates the mental health conditions connected to the posts. To achieve this, we develop a multi-task learning model that leverages, for each post, both the latent embedding space of words and topics for prediction with a message passing mechanism enabling the sharing of information for related tasks. We train the model through an active learning approach in order to tackle the lack of standardized fine-grained label data for this specific task.