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

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Authors: Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder

Appeared in: The 13th Language Resources and Evaluation Conference (LREC 2022)

Links/IDs:
smac.pub: lrec2022-tbd

Abstract:

Social media are heavily used by many users to share their mental health concerns and diagnoses. This 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-average versus below-average 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.

BibTeX @inproceedings{kulkarni:lrec2022-tbd, author = {Kulkarni, Hrishikesh and MacAvaney, Sean and Goharian, Nazli and Frieder, Ophir}, title = {TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users}, booktitle = {The 13th Language Resources and Evaluation Conference}, year = {2022} }