Using Twitter Social Media for Depression Detection in the Canadian Population
Bibliographic record
Abstract
Depression is a serious public health problem, and an economic burden for the society. Therefore, identifying individuals with depression and providing the support for the people in need is a crucial step for creating a healthier environment. In this research, we explore the task of detecting signs of depression from tweets where people often express their feelings, thoughts, interests, and opinions. We utilize personal narratives collected users with self-reported depression to build a model suitable for predicting depression in a sample of Twitter users that is representative for the Canadian population. The training dataset contains 1,402 users who self-reported depression diagnoses during 2016. Using classical machine learning techniques, we achieved 0.961 F1-score using 10-fold cross validation. We used the CLPsych 2015 dataset as a test dataset, and we reached 0.898 F1-score using deep learning models. Then, we applied the same model on a population sample and obtained a result in line with the depression statistics reported by Statistics Canada for 2015.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".