HarmonyNet: Navigating hate speech detection
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
In the digital era, social media platforms have become central to communication across various domains. However, the vast spread of unregulated content often leads to the prevalence of hate speech and toxicity. Existing methods to detect this toxicity struggle with context sensitivity, accommodating diverse dialects, and adapting to varied communication styles. To tackle these challenges, we introduce an ensemble classifier that leverages the strengths of language models and traditional deep neural network architectures for more effective hate speech detection on social media. Our evaluations show that this hybrid approach outperforms individual models and exhibits robustness against adversarial attacks. Future efforts will aim to enhance the model’s architecture to further boost its efficiency and extend its capability to recognize hate speech across an even wider range of languages and dialects. • H armonyNet is a ensemble model that can detect hate speech. • H armonyNet is robust and can handle perturbations in texts. • H armonyNet shows better performance compared to individual models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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