  Depression detection from user-generated content on the internet has been a
long-lasting topic of interest in the research community, providing valuable
screening tools for psychologists. The ubiquitous use of social media platforms
lays out the perfect avenue for exploring mental health manifestations in posts
and interactions with other users. Current methods for depression detection
from social media mainly focus on text processing, and only a few also utilize
images posted by users. In this work, we propose a flexible time-enriched
multimodal transformer architecture for detecting depression from social media
posts, using pretrained models for extracting image and text embeddings. Our
model operates directly at the user-level, and we enrich it with the relative
time between posts by using time2vec positional embeddings. Moreover, we
propose another model variant, which can operate on randomly sampled and
unordered sets of posts to be more robust to dataset noise. We show that our
method, using EmoBERTa and CLIP embeddings, surpasses other methods on two
multimodal datasets, obtaining state-of-the-art results of 0.931 F1 score on a
popular multimodal Twitter dataset, and 0.902 F1 score on the only multimodal
Reddit dataset.
