TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users

Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder


Abstract
Social media are heavily used by many users to share their mental health concerns and diagnoses. This trend has turned social media into a large-scale resource for researchers focused on detecting mental health conditions. Social media usage varies considerably across individuals. Thus, classification of patterns, including detecting signs of depression, must account for such variation. We address the disparity in classification effectiveness for users with little activity (e.g., new users). Our evaluation, performed on a large-scale dataset, shows considerable detection discrepancy based on user posting frequency. For instance, the F1 detection score of users with an above-median versus below-median number of posts is greater than double (0.803 vs 0.365) using a conventional CNN-based model; similar results were observed on lexical and transformer-based classifiers. To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has. This technique alone reduces the margin between users with many and few posts, on average, by 45% across all methods and increases overall performance, on average, by 33%. These findings emphasize the importance of evaluating and tuning natural language systems for potentially vulnerable populations.
Anthology ID:
2022.lrec-1.232
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2157–2165
Language:
URL:
https://aclanthology.org/2022.lrec-1.232
DOI:
Bibkey:
Cite (ACL):
Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, and Ophir Frieder. 2022. TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2157–2165, Marseille, France. European Language Resources Association.
Cite (Informal):
TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users (Kulkarni et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.232.pdf
Code
 georgetown-ir-lab/lrec2022-tbd3