Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts
Bibliographic record
Abstract
The rising prevalence of mental health disorders such as depression, anxiety, and bipolar disorder underscores the urgent need for effective tools to enable early detection and intervention. Social media platforms like Reddit offer a rich source of user-generated content that reflects emotional and behavioral patterns, making them valuable for mental health analysis. However, many existing social media-based approaches focus solely on textual or audiovisual features, often overlooking temporal posting behaviors that can provide crucial contextual cues. Addressing this gap, this study proposes a multi-modal deep learning framework that integrates both linguistic and temporal features from social media posts to detect early signs of mental health crises. The proposed architecture, named DABLNet, utilizes social media post text and timestamp information as input to model the sequential dependencies between user behavior and various mental health conditions. DABLNet consists of a Bi-directional LSTM (BiLSTM) to process textual content, an LSTM to process its temporal data, a cross-modal attention module to fuse outputs from both networks, and a dense layer for classification. This fusion enables context-aware prediction of mental health states. The model is trained and evaluated on a dataset of labeled Reddit posts, which were preprocessed through text cleaning, temporal feature scaling, and label encoding. Experimental results show that the proposed network outperforms traditional models, achieving a test accuracy of 75.96% and an F1-score of 73.76%. These findings highlight the benefits of combining temporal dynamics alongside textual information and provide a solid foundation for future improvements that use more sophisticated attention mechanisms or new data modalities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".