DEEP LEARNING APPLICATIONS IN SOCIAL WORK FOR ENHANCING COMMUNITY ORGANIZING AND SOCIAL ACTION THROUGH TECHNOLOGY
Bibliographic record
Abstract
Abstract This research discusses the relevance of deep learning techniques in understanding community sentiment toward mental health awareness on social media. Specifically, data from Twitter, Facebook, and Reddit were analyzed using Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Graph Neural Networks (GNNs), and Transformer Networks. Perplexity, BLEU score, AUC-PR, precision, recall, ROC-AUC, MAP, F1, and accuracy scores were calculated using this data. The study's results have shown that LSTM and Transformer Networks demonstrate exceptional performance. LSTM achieved a precision of 0.82, recall of 0.79, and effective ROC-AUC. Transformer Networks also provided accurate insights into social media posts related to mental health concerns. In conclusion, these deep learning methods aid in identifying mental health issues. The implications of these insights for future social work and community organizing include improving the targeting of health campaigns, better resource deployment strategies, and promoting mental health awareness more effectively. Overall, the research indicates that advanced deep learning methods can greatly benefit social work by providing accurate, data-driven insights into online community sentiments and enabling precise mental health support programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".