Summary of Ax-to-grind Urdu: Benchmark Dataset For Urdu Fake News Detection, by Sheetal Harris et al.
Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection
by Sheetal Harris, Jinshuo Liu, Hassan Jalil Hadi, Yue Cao
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel dataset for Fake News Detection (FND) in Urdu, addressing the lack of publicly available datasets for this task. The authors curate Ax-to-Grind Urdu, a large-scale dataset containing 10,083 news articles from leading Pakistani and Indian newspapers and news channels, annotated by expert journalists. The dataset is benchmarked with ensemble models based on mBERT, XLNet, and XLM RoBERTa, trained on multilingual large corpora. The results are evaluated using F1-score, accuracy, precision, recall, and MCC value. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake News (FN) detection is crucial for maintaining accurate information online. In Urdu, the lack of available datasets and lexical resources makes it challenging to detect FN. This paper addresses this gap by creating the largest publicly available dataset for Urdu Fake News Detection (FND), Ax-to-Grind Urdu. The dataset includes 10,083 fake and real news articles from various domains, collected from trusted sources in Pakistan and India. |
Keywords
» Artificial intelligence » F1 score » Precision » Recall
