README.md · chengxuphd/liar2 at main

metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - en
size_categories:
  - 10K<n<100K

LIAR2

The LIAR 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". The LIAR2 dataset can be accessed at Huggingface and Github,

Example Usage

You can load each of the subset as follows:

import datasets

dataset = "chengxuphd/liar2"
dataset = datasets.load_dataset(dataset)

statement_train, y_train = dataset["train"]["statement"], dataset["train"]["label"]
statement_val, y_train = dataset["validation"]["statement"], dataset["validation"]["label"]
statement_test, y_test = dataset["test"]["statement"], dataset["test"]["label"]

Citation

If you find our work useful in your research, please consider citing:

@article{xu2024enhanced,
  author={Xu, Cheng and Kechadi, M-Tahar},
  journal={IEEE Access}, 
  title={An Enhanced Fake News Detection System With Fuzzy Deep Learning}, 
  year={2024},
  volume={12},
  number={},
  pages={88006-88021},
  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},
  url={https://doi.org/10.1109/ACCESS.2024.3418340},
  doi={10.1109/ACCESS.2024.3418340}}

@inproceedings{xu2023fuzzy,
   author = {Xu, Cheng and Kechadi, M-Tahar},
   title = {Fuzzy Deep Hybrid Network for Fake News Detection},
   year = {2023},
   isbn = {9798400708916},
   publisher = {Association for Computing Machinery},
   address = {New York, NY, USA},
   url = {https://doi.org/10.1145/3628797.3628971},
   doi = {10.1145/3628797.3628971},
   booktitle = {Proceedings of the 12th International Symposium on Information and Communication Technology},
   pages = {118–125},
   numpages = {8},
   keywords = {Classification Systems, Deep Learning, Hybrid Learning Models, Fuzzy Deep Learning, Fake News Detection},
   location = {<conf-loc>, <city>Ho Chi Minh</city>, <country>Vietnam</country>, </conf-loc>},
   series = {SOICT '23}
}