A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Language toxicity identification presents a gray area in the ethical debate surrounding freedom of speech and censorship. Today's social media landscape is littered with unfiltered content that can be anywhere from slightly abusive to hate inducing. In response, we focused on training a multi-label classifier to detect both the type and level of toxicity in online content. This content is typically colloquial and conversational in style. Its classification therefore requires huge amounts of annotated data due to its variability and inconsistency. We compare standard methods of text classification in this task. A conventional one-vs-rest SVM classifier with character and word level frequency-based representation of text reaches 0.9763 ROC AUC score. We demonstrated that leveraging more advanced technologies such as word embeddings, recurrent neural networks, attention mechanism, stacking of classifiers and semi-supervised training can improve the ROC AUC score of classification to 0.9862. We suggest that in order to choose the right model one has to consider the accuracy of models as well as inference complexity based on the application.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it