Understanding Depression and Suicide Through Words: Analyzing Reddit Posts with Topic Modelling
Bibliographic record
Abstract
This research presents an approach for assessing depression and suicidal concerns using Natural Language Processing (NLP) and topic modelling on Reddit posts. We have applied algorithms such as LDA and LSA to two benchmark datasets, SDCNL and CSSRS, to uncover the semantic structures within the text. Our findings indicate that LDA outperformed LSA’s topic coherence in the SDCNL’s suicide-related task, by capturing more meaningful themes and achieving an optimal coherence score of 0.6633 with 7 topics. On the contrary, both models showed limited performance with the depression posts, due to the general and passive language. Both models demonstrated complementary strengths in CSSRS, LSA outperformed LDA on emotion-driven labels, such as Supportive and Attempt, whereas LDA successfully identified unique and instructive topics for labels like Indicator and Ideation. The coherence score was not the decisive factor in distinguishing the performance of LDA and LSA in this dataset. The results highlight the potential of topic modelling as a tool for mental health monitoring, offering unique patterns associated with depression and suicide risk.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".