\section{Conclusion}
In this paper we introduce Multi-Explainable TemporalNet (METN), a novel TCN-based multimodal transformer network incorporating temporal embeddings and attention mechanisms for enhanced user-level depression detection on social media datasets. The model utilizes pretrained foundation models for text and image embeddings; EmoBERTA and CLIP and attention maps for interpretability. We leverage TCN encoder which is able to capture long-range dependencies and input sequences efficiently as it computes all outputs in parallel during training. Extensive experiments demonstrate METN's state-of-the-art performance on multimodal Twitter and Reddit datasets, with F1 scores of 0.945 and 0.913, respectively. The model's interpretable compatibility and attention mechanisms provide insights into the contribution of different features to the final prediction, offering a promising approach for user-level depression detection with improved accuracy and understanding. This model can be potentially be used as an assistive technology for early depression detection among users on social media platforms.
\section{Acknowledgement}This work is supported by the Research Funds from Health@InnoHK Program launched by Innovation Technology Commission of the Hong Kong SAR, China.
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