SSL-GAN-RoBERTa: A robust semi-supervised model for detecting Anti-Asian COVID-19 hate speech on social media
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
Abstract Anti-Asian speech during the COVID-19 pandemic has been a serious problem with severe consequences. A hate speech wave swept social media platforms. The timely detection of Anti-Asian COVID-19-related hate speech is of utmost importance, not only to allow the application of preventive mechanisms but also to anticipate and possibly prevent other similar discriminatory situations. In this paper, we address the problem of detecting Anti-Asian COVID-19-related hate speech from social media data. Previous approaches that tackled this problem used a transformer-based model, BERT/RoBERTa, trained on the homologous annotated dataset and achieved good performance on this task. However, this requires extensive and annotated datasets with a strong connection to the topic. Both goals are difficult to meet without employing reliable, vast, and costly resources. In this paper, we propose a robust semi-supervised model, SSL-GAN-RoBERTa, that learns from a limited heterogeneous dataset and whose performance is further enhanced by using vast amounts of unlabeled data from another related domain. Compared with the RoBERTa baseline model, the experimental results show that the model has substantial performance gains in terms of Accuracy and Macro-F1 score in different scenarios that use data from different domains. Our proposed model achieves state-of-the-art performance results while efficiently using unlabeled data, showing promising applicability to other complex classification tasks where large amounts of labeled examples are difficult to obtain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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