Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection
<|reference_start|>Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection: Misinformation can seriously impact society, affecting anything from public opinion to institutional confidence and the political horizon of a state. Fake News (FN) proliferation on online websites and Online Social Networks (OSNs) has increased profusely. Various fact-checking websites include news in English and barely provide information about FN in regional languages. Thus the Urdu FN purveyors cannot be discerned using factchecking portals. SOTA approaches for Fake News Detection (FND) count upon appropriately labelled and large datasets. FND in regional and resource-constrained languages lags due to the lack of limited-sized datasets and legitimate lexical resources. The previous datasets for Urdu FND are limited-sized, domain-restricted, publicly unavailable and not manually verified where the news is translated from English into Urdu. In this paper, we curate and contribute the first largest publicly available dataset for Urdu FND, Ax-to-Grind Urdu, to bridge the identified gaps and limitations of existing Urdu datasets in the literature. It constitutes 10,083 fake and real news on fifteen domains collected from leading and authentic Urdu newspapers and news channel websites in Pakistan and India. FN for the Ax-to-Grind dataset is collected from websites and crowdsourcing. The dataset contains news items in Urdu from the year 2017 to the year 2023. Expert journalists annotated the dataset. We benchmark the dataset with an ensemble model of mBERT,XLNet, and XLM RoBERTa. The selected models are originally trained on multilingual large corpora. The results of the proposed model are based on performance metrics, F1-score, accuracy, precision, recall and MCC value.<|reference_end|>
@article{harris2024ax-to-grind,
    title={Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection},
    author={Sheetal Harris, Jinshuo Liu, Hassan Jalil Hadi, Yue Cao},
    journal={arXiv preprint arXiv:2403.14037},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14037},
    primaryClass={cs.CL cs.AI}
}