Depression is a common and serious mental disorder that causes a person to have sad or hopeless feelings in his/her daily life. With the rapid development of social media, people tend to express their thoughts or emotions on the social platform. Different social platforms have various formats of data presentation, which makes huge and diverse data available for analysis by researchers. In our study, we aim to detect users with depressive tendency on Instagram. We create a depression dictionary for automatically collecting data of depressive and non-depressive users. In terms of the prediction model, we construct a multimodal system, which utilizes image, text and behavior features to predict the aggregated depression score of each post on Instagram. Considering the time interval between posts, we propose a two-stage detection mechanism for detecting depressive users. Experimental results demonstrate that our proposed methods can achieve up to 0.835 F1-score for detecting depressive users. It can therefore serve as an early depression detector for a timely treatment before it becomes severe.

中文翻译：

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考虑帖子时间间隔的instagram多模态抑郁检测

抑郁症是一种常见且严重的精神障碍，会导致人们在日常生活中产生悲伤或绝望的感觉。随着社交媒体的快速发展，人们倾向于在社交平台上表达自己的想法或情感。不同的社交平台有不同的数据呈现形式，这使得研究人员可以分析海量多样的数据。在我们的研究中，我们的目标是检测 Instagram 上有抑郁倾向的用户。我们创建了一个抑郁词典，用于自动收集抑郁和非抑郁用户的数据。在预测模型方面，我们构建了一个多模态系统，该系统利用图像、文本和行为特征来预测 Instagram 上每个帖子的总体抑郁评分。考虑到帖子之间的时间间隔，我们提出了一种用于检测抑郁用户的两阶段检测机制。实验结果表明，我们提出的方法可以达到 0.835 F1-score 来检测抑郁用户。因此，它可以作为早期抑郁症检测器，以便在它变得严重之前及时治疗。