Empirical Study on Improving Hate Speech Detection: Novel BERT based One-Versus-All Classification Approach (BOVAC) with a Novel Performance Metric: Global Performance (GP)
Bibliographic record
Abstract
Text classification is an application of natural language processing (NLP) which involves the automated processing of text data for the purpose of extracting features, classifying opinions or performing sentiment analysis. Attempting to improve the task of automated detection of hate speech and the understanding of the framework upon which it operates, I present my thesis in which: I explore an approach I call BERT-based one-versus-all text classification (BOVAC) for improving the task of hate speech detection. The performance of the proposed approach is assessed based on an empirical study on a dataset which was previously constructed, cleaned and manually labeled by Davidson and colleagues (Davidson et al., 2017). In addition to presenting an approach to improve hate speech detection, I propose the use of a new performance metric I call global performance (GP) to improve the process of assessing the performance of hate speech detection and text classification models.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".