Decoding depression: Analyzing social network insights for depression severity assessment with transformers and explainable AI
Bibliographic record
Abstract
Depression is a mental state characterized by recurrent feelings of melancholy, hopelessness, and disinterest in activities, having a significant negative influence on everyday functioning and general well-being. Millions of users express their thoughts and emotions on social media platforms, which can be used as a rich source of data for early detection of depression. In this connection, this work leverages an ensemble of transformer-based architectures for quantifying the severity of depression from social media posts into four categories- non-depressed, mild, moderate, and severe. At first, a diverse range of preprocessing techniques is employed to enhance the quality and relevance of the input. Then, the preprocessed samples are passed through three variants of transformer-based models, namely vanilla BERT, BERTweet, and ALBERT, for generating predictions, which are combined using a weighted soft-voting approach. We conduct a comprehensive explainability analysis to gain deeper insights into the decision-making process, examining both local and global perspectives. Furthermore, to the best of our knowledge, we are the first ones to explore the extent to which a Large Language Model (LLM) like ‘ChatGPT’ can perform this task. Evaluation of the model on the publicly available ‘DEPTWEET’ dataset produces state-of-the-art performance with 13.5% improvement in AUC-ROC score.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".