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Depression detection from user-generated content on the internet has been along-lasting topic of interest in the research community, providing valuablescreening tools for psychologists. The ubiquitous use of social media platformslays out the perfect avenue for exploring mental health manifestations in postsand interactions with other users. Current methods for depression detectionfrom social media mainly focus on text processing, and only a few also utilizeimages posted by users. In this work, we propose a flexible time-enrichedmultimodal transformer architecture for detecting depression from social mediaposts, using pretrained models for extracting image and text embeddings. Ourmodel operates directly at the user-level, and we enrich it with the relativetime between posts by using time2vec positional embeddings. Moreover, wepropose another model variant, which can operate on randomly sampled andunordered sets of posts to be more robust to dataset noise. We show that ourmethod, using EmoBERTa and CLIP embeddings, surpasses other methods on twomultimodal datasets, obtaining state-of-the-art results of 0.931 F1 score on apopular multimodal Twitter dataset, and 0.902 F1 score on the only multimodalReddit dataset.

It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers