MUCS@LT-EDI2023: Detecting Signs of Depression in Social Media Text

Sharal Coelho, Asha Hegde, Kavya G, Hosahalli Lakshmaiah Shashirekha

Abstract
Depression can lead to significant changes in individuals’ posts on social media which is a important task to identify. Automated techniques must be created for the identification task as manually analyzing the growing volume of social media data is time-consuming. To address the signs of depression posts on social media, in this paper, we - team MUCS, describe a Transfer Learning (TL) model and Machine Learning (ML) models submitted to “Detecting Signs of Depression from Social Media Text” shared task organised by DepSign-LT-EDI@RANLP-2023. The TL model is trained using raw text Bidirectional Encoder Representations from Transformers (BERT) and the ML model is trained using Term Frequency-Inverse Document Frequency (TF-IDF) features separately. Among these three models, the TL model performed better with a macro averaged F1-score of 0.361 and placed 20th rank in the shared task.
Anthology ID:
2023.ltedi-1.45
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:
295–299
Language:
URL:
https://aclanthology.org/2023.ltedi-1.45
DOI:
Bibkey:
Cite (ACL):
Sharal Coelho, Asha Hegde, Kavya G, and Hosahalli Lakshmaiah Shashirekha. 2023. MUCS@LT-EDI2023: Detecting Signs of Depression in Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 295–299, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
MUCS@LT-EDI2023: Detecting Signs of Depression in Social Media Text (Coelho et al., LTEDI-WS 2023)
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