Abstract
Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. Given that there is a large number of people with excessive social media usage, then there is a significant population of potentially undiagnosed users and posts that they create. In this paper, we propose a depression detection system using a semi-supervised learning technique. Namely, we use a trained model to classify a large number of unlabelled social media posts from Reddit, then use these generated labels to train a more powerful classifier. We demonstrate our framework on Detecting Signs of Depression from Social Media Text - LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.- Anthology ID:
- 2023.ltedi-1.29
- Volume:
- Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Bharathi R. Chakravarthi, B. Bharathi, Joephine Griffith, Kalika Bali, Paul Buitelaar
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 192–197
- Language:
- URL:
- https://aclanthology.org/2023.ltedi-1.29
- DOI:
- Cite (ACL):
- Dean Ninalga. 2023. Cordyceps@LT-EDI : Depression Detection with Reddit and Self-training. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 192–197, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Cordyceps@LT-EDI : Depression Detection with Reddit and Self-training (Ninalga, LTEDI-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.ltedi-1.29.pdf