worldbank/naija-xlm-twitter-base-hate

NaijaXLM-T-base Hate

This is a NaijaXLM-T base model finetuned on Nigerian tweets annotated for hate speech detection. The model is described and evaluated in the reference paper and was developed by @pvcastro and @manueltonneau.

Model Details

Model Description

• Model type: xlm-roberta
• Language(s) (NLP): (Nigerian) English, Nigerian Pidgin, Hausa, Yoruba, Igbo
• Finetuned from model: worldbank/naija-xlm-twitter-base

Model Sources [optional]

• Repository: https://github.com/worldbank/NaijaHate
• Paper: https://aclanthology.org/2024.acl-long.488/

Training Details

Training Data

This model was finetuned on the stratified (dataset=='stratified') and active learning (dataset=='al') subset of NaijaHate.

Training Procedure and Evaluation

We perform a 90-10 train-test split and conduct a 5-fold cross-validation with 5 learning rates ranging from 1e-5 to 5e-5. Each fold is trained using 3 different seeds. The train-test split is repeated for 10 different seeds, and the evaluation metrics are averaged across the 10 seeds.

We evaluate model performance on three datasets: the holdout sample from the train-test splits as well as the top-scored sample (dataset=='eval') and the random sample (dataset=='random') from NaijaHate.

Model	Holdout	Top-scored	Random
GPT-3.5, ZSL	-	60.3±2.7	3.1±1.2
Perspective API	-	60.2±3.5	4.3±2.6
XLM-T	84.2 ± 0.6	51.8 ± 0.7	0.6 ± 0.1
XLM-T	62.0 ± 2.3	68.9 ± 0.8	3.3 ± 0.6
XLM-T	70.5 ± 3.7	63.7 ± 1.1	1.9 ± 0.5
DeBERTaV3	82.3 ± 2.3	85.3 ± 0.8	29.7 ± 4.1
XLM-R	76.7 ± 2.5	83.6 ± 0.8	22.1 ± 3.7
mDeBERTaV3	29.2 ± 2.0	49.6 ± 1.0	0.2 ± 0.0
Conv. BERT	79.2 ± 2.3	86.2 ± 0.8	22.6 ± 3.6
BERTweet	83.6 ± 2.0	88.5 ± 0.6	34.0 ± 4.4
XLM-T	79.0 ± 2.4	84.5 ± 0.9	22.5 ± 3.7
AfriBERTa	70.1 ± 2.7	80.1 ± 0.9	12.5 ± 2.8
AfroXLM-R	79.7 ± 2.3	86.1 ± 0.8	24.7 ± 4.0
XLM-R Naija	77.0 ± 2.5	83.5 ± 0.8	19.1 ± 3.4
NaijaXLM-T	83.4 ± 2.1	89.3 ± 0.7	33.7 ± 4.5

For more information on the evaluation, please read the reference paper.

BibTeX entry and citation information

Please cite the reference paper if you use this model.

@inproceedings{tonneau-etal-2024-naijahate,
    title = "{N}aija{H}ate: Evaluating Hate Speech Detection on {N}igerian {T}witter Using Representative Data",
    author = "Tonneau, Manuel  and
      Quinta De Castro, Pedro  and
      Lasri, Karim  and
      Farouq, Ibrahim  and
      Subramanian, Lakshmi  and
      Orozco-Olvera, Victor  and
      Fraiberger, Samuel",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.488",
    pages = "9020--9040",
    abstract = "To address the global issue of online hate, hate speech detection (HSD) systems are typically developed on datasets from the United States, thereby failing to generalize to English dialects from the Majority World. Furthermore, HSD models are often evaluated on non-representative samples, raising concerns about overestimating model performance in real-world settings. In this work, we introduce NaijaHate, the first dataset annotated for HSD which contains a representative sample of Nigerian tweets. We demonstrate that HSD evaluated on biased datasets traditionally used in the literature consistently overestimates real-world performance by at least two-fold. We then propose NaijaXLM-T, a pretrained model tailored to the Nigerian Twitter context, and establish the key role played by domain-adaptive pretraining and finetuning in maximizing HSD performance. Finally, owing to the modest performance of HSD systems in real-world conditions, we find that content moderators would need to review about ten thousand Nigerian tweets flagged as hateful daily to moderate 60{\%} of all hateful content, highlighting the challenges of moderating hate speech at scale as social media usage continues to grow globally. Taken together, these results pave the way towards robust HSD systems and a better protection of social media users from hateful content in low-resource settings.",
}