It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers
Ana-Maria Bucur, Adrian Cosma, Paolo Rosso, Liviu P. Dinu

TL;DR
This paper introduces a novel time-enriched multimodal transformer model that combines text and image embeddings with temporal information to improve depression detection accuracy on social media data.
Contribution
The work presents a flexible transformer architecture that integrates time-aware embeddings and can handle unordered posts, advancing multimodal depression detection methods.
Findings
Achieved state-of-the-art F1 scores of 0.931 on Twitter dataset.
Achieved state-of-the-art F1 scores of 0.902 on Reddit dataset.
Demonstrated robustness to dataset noise with unordered post sampling.
Abstract
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…
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Taxonomy
TopicsMental Health via Writing · Sentiment Analysis and Opinion Mining · Digital Mental Health Interventions
MethodsContrastive Language-Image Pre-training
