Abstract
In the digital age, there is a growing demand for accurate online mental health support, yet current platforms struggle with text-based interaction analysis. This research introduces MeTAN, an innovative approach for automatic depression detection in text, offering a private and convenient method for individuals to assess their mental health early on, before professional engagement. Unlike traditional black-box deep learning methods focused solely on classification, MeTAN prioritizes explainability in health research, which is critical for high-stakes decisions in mental health. It leverages a novel encoder that integrates hierarchical attention mechanisms, metaphorical interpretation, and temporal features to detect depression and identify key textual indicators in tweets. MeTAN aims to assist psychologists by detecting and interpreting emotional patterns in social media text, thereby enhancing diagnosis in virtual settings where anonymity is paramount. Experimental results demonstrate that MeTAN outperforms existing approaches with fewer parameters, showcasing its efficacy in depression detection.
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Acknowledgments
The authors would like to thank all authors’ contributions to this work. Besides, we would like to extend our gratitude to the authors of [5] for generously making their model open source and publicly available. This invaluable resource facilitated our experimentation and analysis, enabling us to build upon their work and contribute to the advancement of depression detection.
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Kurian, R.S., Chaudhary, C., Nambiar, A.U., Sunny, A. (2025). MeTAN: Metaphoric Temporal Attention Network for Depression Detection on Social Media. In: Barhamgi, M., Wang, H., Wang, X. (eds) Web Information Systems Engineering – WISE 2024. WISE 2024. Lecture Notes in Computer Science, vol 15437. Springer, Singapore. https://doi.org/10.1007/978-981-96-0567-5_8
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