Deep Learning Based Misogynistic Bangla Text Identification from Social Media
Bibliographic record
Abstract
Misogyny is characterized by hostility, hatred, aversion, intimidation, and violence against women. With the rise of social media, it has become one of the most convenient platforms for expressing woman-hating speech. As a result, misogyny is gaining appeal and societal standards are being violated. With millions of Bangladeshi Facebook users, misogyny is growing increasingly prevalent in Bangla as well. In this paper, we have proposed automatically identifying misogynistic content in Bangla on social media platforms in order to evaluate the problem's challenges. As there is no existing Bangla dataset for analyzing misogynistic text, we generated our own. We have applied various deep-learning algorithms to improve the classification of misogynistic text categories. LSTM and RNN models are used for designing the model architecture in deep learning. Models are evaluated using the confusion matrix, accuracy, and f1-scores. The results indicate that LSTM outperforms RNN in terms of accuracy by 67 %.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".