Hate Speech Detection Eval

hate-speech-detection-eval

ToxSyn: Reducing Bias in Hate Speech Detection via Synthetic Minority Data in Brazilian Portuguese — Brito et al. (2025) (arXiv:2506.10245, 2025)

What this evaluates

Evaluates binary and multi-label hate speech detection models on Brazilian Portuguese text. It specifically probes a model's sensitivity to targeted minority groups and its ranking quality under severe class imbalance.

Datasets

• ToxiGen-PT — total 8960; splits: test (8960)
• Portuguese Superset Benchmark — total 6002; splits: test (6002)
• HateBR — total ?; splits: test (-1)
• OLID-BR — total ?; splits: test (-1)
• TuPy-E — total ?; splits: test (-1)
• ToLD-BR — total ?; splits: test (-1)

Metrics

• Macro-Recall (primary) — range: [0, 1]
  • Unweighted mean of recall scores computed independently for each class (toxic/non-toxic or each minority group).
• ROC AUC — range: [0, 1]
  • Area under the Receiver Operating Characteristic curve, measuring the model's ability to rank positive instances higher than negative ones across all classification thresholds.

Input / output format

Input: Raw text strings in Brazilian Portuguese (social media posts/comments).

Output: Binary label (toxic vs. non-toxic) or a multi-label vector indicating presence of specific protected minority group targets.

Scoring recipe

def compute_macro_recall(y_true, y_pred, num_classes):
    recalls = []
    for c in range(num_classes):
        tp = sum(1 for t, p in zip(y_true, y_pred) if t == c and p == c)
        fn = sum(1 for t, p in zip(y_true, y_pred) if t == c and p != c)
        recalls.append(tp / (tp + fn) if (tp + fn) > 0 else 0.0)
    return sum(recalls) / len(recalls)

def compute_roc_auc(y_true, y_scores):
    fpr, tpr, _ = roc_curve(y_true, y_scores)
    return auc(fpr, tpr)

Common pitfalls

• Severe class imbalance in hate speech corpora makes accuracy misleading; macro-recall is required to capture minority group sensitivity.
• Existing benchmarks only label protected groups when content is explicitly toxic, leaving neutral/supportive references unlabeled and skewing evaluation.
• Translated datasets contain unnaturalized phrasing and culturally mismatched slurs that may not reflect authentic Brazilian Portuguese usage.

Evidence (verbatim from paper)

Due to class imbalance, we report macro-averaged recall (to measure per-class sensitivity) and ROC AUC (to assess ranking quality).

Citation

@misc{brito2025toxsyn,
  title={ToxSyn: Reducing Bias in Hate Speech Detection via Synthetic Minority Data in Brazilian Portuguese},
  author={Brito et al. (2025)},
  year={2025},
  note={arXiv:2506.10245}
}

• arXiv: 2506.10245