Common N-Gram Method (CNG): A Promising Approach to Detecting Mental Health Disorders on Social Media
Bibliographic record
Abstract
Technology and social media's growing prevalence, especially during the COVID-19 pandemic, has contributed to a rise in mental health issues. This research discusses novel applications of natural language processing which can help develop more effective and accessible diagnostic tools for mental health illnesses. To enhance the realism of our model, we created a biased dataset that reflects the real-world ratios of mental illness prevalence. The proposed solution is the Common N-grams (CNG) method that offers comparable results to the state-of-the-art CNN-LSTM model and is less resource-intensive. The CNG method performs better than the CNN-LSTM model and Support Vector Machine (SVM), baseline model, in multiclassification tasks. The CNN-LSTM surpasses performance in binary tasks compared to the best score reported in the previous study with the same dataset. The study also highlights the usefulness of the Relative N-Gram Signature method to analyze the classification decision of the CNG technique. The proposed solutions offer practical and accessible options for individuals seeking reliable and accurate mental health support.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".