Linguistic models of abusive language
Bibliographic record
Abstract
Abusive language and hate speech that incites violence is a growing problem for governments trying to manage social media posts on the internet. Such language incites civil disorder by angering target individuals and groups, and tends to reinforce the identity and cohesiveness of those who use it. Abusive language is an intelligence target because it can be a leading indicator of violence. Black box predictors can achieve high accuracy but they reveal nothing about the structures, both social and technical, that underlie abusive language. We build a set of abusive language predictors leveraging both social constructs such as otherness, and linguistic properties. A stacking predictor then determines the most significant component predictors. We achieve prediction accuracy of 96.5%, with an 88% accuracy for abusive documents. This is an improvement of more than 16% points in detecting abusive documents compared to a popular empirically derived predictors, and provides insights into the mechanics of abusive language.
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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 | 0.001 | 0.002 |
| 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".