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FaKe news Text Collections (FKTC)

Library to use fakenews text collections

If you use any part of this code in your research, please cite it using the following BibTex entry

@inproceedings{ref:Golo2021,
  title={Learning textual representations from multiple modalities to detect fake news through one-class learning},
  author={Gôlo, Marcos and Caravanti, Mariana and Rossi, Rafael and Rezende, Solange and Nogueira, Bruno and Marcacini, Ricardo},
  booktitle={Proceedings of the Brazilian Symposium on Multimedia and the Web},
  pages={197--204},
  year={2021}
}

How To use

!pip install git+https://github.com/GoloMarcos/FKTC/
from FakeNewsTextCollections import datasets
datasets_dictionary = datasets.load()
df = datasets_dictionary['fcn']

Datasets

• Fact Checked News (fcn) : RIBEIRO, V. H. P. Identificação de notícias falsas em língua portuguesa. Monografia (TCC). Universidade Federal de Mato Grosso do Sul, 2019.

• Fake News Net (fnn) : Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, v. 8, n. 3, p. 171–188, 2020.

• Fake BR (fakebr) : MONTEIRO, R.; SANTOS, R.; PARDO, T.; ALMEIDA, T. de; RUIZ, E.; VALE, O. Contributions to the study of fake news in portuguese: New corpus and automatic detection results. In: PROPOR 2018: International Conference on Computational Processing of the Portuguese Language. [S.l.]: Springer, 2018. p. 324–334.

• Fake News Corpus 0 (fnc0) : collection derived from https://github.com/several27/FakeNewsCorpus

• Fake News Corpus 1 (fnc1) : collection derived from https://github.com/several27/FakeNewsCorpus

• Fake News Corpus 2 (fnc2) : collection derived from https://github.com/several27/FakeNewsCorpus

Datasets Characteristics

Columns from DataFrame

• index: id
• text: content of the news
• class: fake (1) | real (-1)
• folds: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
• features: 63 features extracted using Linguistic Inquiry and Word Count (LIWC)
• features_normalized: 63 features with normalization extracted using LIWC
• BERT: embedding with 1024 real values
• DistilBERT: embedding with 768 real values
• Multilingual DistilBERT: embedding with 512 real values
• RoBERTa: embedding with 1024 real values

Linguistic Inquiry and Word Count (LIWC)

• Pennebaker, James W., et al. The development and psychometric properties of LIWC2015. 2015.

We obtain the embeddings with the library sentence_tranformers (v==1.0.4) (https://www.sbert.net/)

• BERT model: bert-large-nli-stsb-mean-tokens
  • Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
• DistilBERT model: distilbert-base-nli-stsb-mean-tokens
  • Sanh, Victor, et al. "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter." arXiv preprint arXiv:1910.01108 (2019).
• RoBERTa model: roberta-large-nli-stsb-mean-tokens
  • Liu, Zhuang, et al. "A Robustly Optimized BERT Pre-training Approach with Post-training." China National Conference on Chinese Computational Linguistics. Springer, Cham, 2021.
• DistilBERT Multilingual model: distiluse-base-multilingual-cased
  • Reimers, Nils, and Iryna Gurevych. "Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.