IIITDWD@LT-EDI-2023 Unveiling Depression: Using pre-trained language models for Harnessing Domain-Specific Features and Context Information

Shankar Biradar, Sunil Saumya, Sanjana Kavatagi

Abstract
Depression has become a common health problem impacting millions of individuals globally. Workplace stress and an unhealthy lifestyle have increased in recent years, leading to an increase in the number of people experiencing depressive symptoms. The spread of the epidemic has further exacerbated the problem. Early detection and precise prediction of depression are critical for early intervention and support for individuals at risk. However, due to the social stigma associated with the illness, many people are afraid to consult healthcare specialists, making early detection practically impossible. As a result, alternative strategies for depression prediction are being investigated, one of which is analyzing users’ social media posting behaviour. The organizers of LT-EDI@RANLP carried out a shared Task to encourage research in this area. Our team participated in the shared task and secured 21st rank with a macro F1 score 0f 0.36. This article provides a summary of the model presented in the shared task.
Anthology ID:
2023.ltedi-1.17
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:
117–123
Language:
URL:
https://aclanthology.org/2023.ltedi-1.17
DOI:
Bibkey:
Cite (ACL):
Shankar Biradar, Sunil Saumya, and Sanjana Kavatagi. 2023. IIITDWD@LT-EDI-2023 Unveiling Depression: Using pre-trained language models for Harnessing Domain-Specific Features and Context Information. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 117–123, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
IIITDWD@LT-EDI-2023 Unveiling Depression: Using pre-trained language models for Harnessing Domain-Specific Features and Context Information (Biradar et al., LTEDI-WS 2023)
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