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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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## LIAR2 |
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The [LIAR](https://doi.org/10.18653/v1/P17-2067) dataset has been widely followed by fake news detection researchers since its release, and along with a great deal of research, the community has provided a variety of feedback on the dataset to improve it. We adopted these feedbacks and released the LIAR2 dataset, a new benchmark dataset of ~23k manually labeled by professional fact-checkers for fake news detection tasks. We have used a split ratio of 8:1:1 to distinguish between the training set, the test set, and the validation set, details of which are provided in the paper of "[An Enhanced Fake News Detection System With Fuzzy Deep Learning](https://doi.org/10.1109/ACCESS.2024.3418340)". The LIAR2 dataset can be accessed at [Huggingface](https://huggingface.co/datasets/chengxuphd/liar2) and [Github](https://github.com/chengxuphd/LIAR2), |
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## Example Usage |
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You can load each of the subset as follows: |
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```python |
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import datasets |
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dataset = "chengxuphd/liar2" |
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dataset = datasets.load_dataset(dataset) |
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statement_train, y_train = dataset["train"]["statement"], dataset["train"]["label"] |
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statement_val, y_train = dataset["validation"]["statement"], dataset["validation"]["label"] |
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statement_test, y_test = dataset["test"]["statement"], dataset["test"]["label"] |
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``` |
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## Citation |
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If you find our work useful in your research, please consider citing: |
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```bibtex |
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@article{xu2024enhanced, |
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author={Xu, Cheng and Kechadi, M-Tahar}, |
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journal={IEEE Access}, |
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title={An Enhanced Fake News Detection System With Fuzzy Deep Learning}, |
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year={2024}, |
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volume={12}, |
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number={}, |
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pages={88006-88021}, |
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keywords={Fake news;Fuzzy logic;Benchmark testing;Social networking (online);Deep learning;Task analysis;Natural language processing;Classification algorithms;Deep learning;fuzzy deep learning;fake news;fake news detection;fact-checking;NLP;classification systems;benchmark}, |
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url={https://doi.org/10.1109/ACCESS.2024.3418340}, |
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doi={10.1109/ACCESS.2024.3418340}} |
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``` |
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```bibtex |
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@inproceedings{xu2023fuzzy, |
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author = {Xu, Cheng and Kechadi, M-Tahar}, |
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title = {Fuzzy Deep Hybrid Network for Fake News Detection}, |
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year = {2023}, |
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isbn = {9798400708916}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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url = {https://doi.org/10.1145/3628797.3628971}, |
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doi = {10.1145/3628797.3628971}, |
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booktitle = {Proceedings of the 12th International Symposium on Information and Communication Technology}, |
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pages = {118–125}, |
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numpages = {8}, |
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keywords = {Classification Systems, Deep Learning, Hybrid Learning Models, Fuzzy Deep Learning, Fake News Detection}, |
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location = {<conf-loc>, <city>Ho Chi Minh</city>, <country>Vietnam</country>, </conf-loc>}, |
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series = {SOICT '23} |
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} |
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``` |
