danghaidang-passau/HateOWS-dataset-LREC2026

OWS Data for LREC 2026

This repository contains the data resources used in the paper:

Toward Generalized Cross-Lingual Hateful Language Detection with Web-Scale Data and Ensemble LLM Annotations (LREC-COLING 2026).

It provides:

• multilingual OpenWebSearch (OWS) raw corpora (DEU, ENG, SPA, VIE)
• an annotated subset with four base LLM annotators + three ensemble labeling strategies
• a LightGBM-derived dataset (training probabilities / features)
• the 16 human-labelled datasets used for validation (train / val counts below)

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Splits

• Raw

  • 4L — 4-language OWS corpus (DEU, ENG, SPA, VIE)
  • deu — German unlabeled subset
  • eng — English unlabeled subset
  • spa — Spanish unlabeled subset

• Annotated

  • annotated — subset with model probabilities and ensemble labels

• LightGBM

  • LightGBM_dataset — LightGBM training probabilities / features (7-dataset LGB training)

• Human 16

  • 16-val-train — 16 datasets used for train and validation splits (tables below)

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How to download splits

1. Load raw splits:

from datasets import load_dataset

repo = "danghaidang-passau/HateOWS-dataset-LREC2026"
ds_raw = load_dataset(repo, "raw")       

# access splits:
ds_4l = ds_raw["4L"]
ds_deu = ds_raw["deu"]
ds_eng = ds_raw["eng"]
ds_spa = ds_raw["spa"]

2. Load annotated splits:

ds_annotated = load_dataset(repo, "annotatedOWS")       
ds_annotated = ds_annotated["train"]

3. Load 16 human train/test splits:

ds_16 = load_dataset(repo, "16-val-train")       
ds_train = ds_16["train"]
ds_test = ds_16["test"]

4. Load LightGBM annotated splits:

ds_lgb = load_dataset(repo, "LightGBM_dataset")       
ds_lgb = ds_lgb["train"]

Language stats (deu, eng, spa)

split	language	rows	token_len_sum
deu	deu	641830	51995390
eng	eng	1598372	119077423
spa	spa	1085275	110562498

Language stats 4L (deu_eng_spa_vie)

split	language	rows	token_len_sum
4L	deu	900000	47262107
4L	eng	1200000	55352705
4L	spa	500000	24724569
4L	vie	186912	9273567

Hate counts on annotated

The table below reports hate counts for four base annotator models and three ensemble methods. For base models, hate is derived as prob_1 >= prob_2 (same class ordering as the original pipeline).

model	hate_count	total_rows	hate_ratio_pct
Qwen2.5-14B	5823	240647	2.42
Gemma2-9B	99200	240647	41.22
Llama3.1-8B	534	240647	0.22
Mistral-7B	5012	240647	2.08
LightGBM Ensemble	3122	240647	1.3
Mean Ensemble	3987	240647	1.66
Vote Ensemble	4707	240647	1.96

Annotation columns (short names)

• Base models: qwen_prob_1, qwen_prob_2, gemma_prob_1, gemma_prob_2, llama_prob_1, llama_prob_2, mistral_prob_1, mistral_prob_2
• Ensemble probs: mean_prob_1, mean_prob_2, lgb_prob_1, lgb_prob_2
• Ensemble labels: mean_label, lgb_label, vote_label
• Shared metadata: text, language, token_len

16 human datasets — train / validation counts (with reference links)

Combined train / validation table and LightGBM usage (✓ indicates dataset used for LightGBM training)

Dataset	Language	Train rows	Val rows	LightGBM	Reference
AHSD	eng	21,783	3,000		https://ojs.aaai.org/index.php/ICWSM/article/view/14955
HateXplain	eng	15,299	3,846	✓	https://ojs.aaai.org/index.php/AAAI/article/view/17745
AbusEval	eng	13,240	860		https://aclanthology.org/2020.lrec-1.760/
Sexism	eng	10,904	2,632	✓	https://ojs.aaai.org/index.php/ICWSM/article/view/18085
GermEval19	deu	9,698	2,507	✓	https://www.zora.uzh.ch/server/api/core/bitstreams/2b6a9186-fb29-48fc-a9c2-e29cafd1949d/content
HateEval-eng	eng	9,000	1,000		https://aclanthology.org/S19-2007/
Gahd	deu	8,797	2,198		https://aclanthology.org/2024.naacl-long.248/
ViHSD	vie	8,061	2,672	✓	https://link.springer.com/chapter/10.1007/978-3-030-79457-6_35
Chileno	spa	7,572	1,928		https://aclanthology.org/2022.woah-1.12/
HateEval-spa	spa	5,309	1,286		https://aclanthology.org/S19-2007/
GermEval18	deu	5,009	3,532		https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/DATA/0B5VML/
Haternet	spa	4,794	1,205		https://aclanthology.org/S19-2007/
HASOC	deu	2,373	526		https://dl.acm.org/doi/10.1145/3368567.3368584
GermEval21	deu	2,071	2,085	✓	https://aclanthology.org/2021.germeval-1.1/
US_election	eng	1,283	1,117	✓	https://aclanthology.org/2021.wassa-1.18/
Covid	eng	1,282	971	✓	https://dl.acm.org/doi/10.1145/3487351.3488324