Adapting Deep Learning Methods for Mental Health Prediction on Social Media

Ivan Sekulic, Michael Strube


Abstract
Mental health poses a significant challenge for an individual’s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users’ mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model’s word-level attention weights.
Anthology ID:
D19-5542
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
322–327
Language:
URL:
https://www.aclweb.org/anthology/D19-5542
DOI:
10.18653/v1/D19-5542
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5542.pdf