Disclaimer

I am not the author of this dataset, this is a modified version of the FactCheck dataset on HuggingFace, the original data is made avaliable by Vargas et. al, 2023 and can be downloaded from the link: https://github.com/franciellevargas/FactNews

Modifications:

The "original" subset contains the unmodified original CSV
The subsets for the task of "bias_prediction" and "factuality_prediction" were splited between train (70%) AND test (30%) by randomly selecting sentences grouped by their id_article. This configuration difers from the authors, who made a 90%/10% 10-fold split on the papers.
Each task contains an unbalanced split (full-train) and the balanced-split (train)

Sentence-Level Annotated Dataset for Predicting Factuality of News and Bias of Media Outlets in Portuguese

Automated fact-checking and news credibility verification at scale require accurate prediction of news factuality and media bias. Here, we introduce a large sentence-level dataset, titled FactNews, composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We used the FactNews to assess the overall reliability of news sources by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, the fine-grained analysis of subjectivity and impartiality of news articles showed promising results for predicting the reliability of the entire media outlet. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, both dataset and baseline were proposed for Brazilian Portuguese. The following table describes in detail the FactNews labels, documents, and stories:

Factual	Quotes	Biased	Total sentences	Total news stories	Total news documents
4,242	1,391	558	6,161	100	300

Sources:

Media 1: Folha de São Paulo
Media 2: Estadão
Media 3: O Globo

Paper Results:

Sentence-Level Media Bias Prediction (90%/10% 10-fold split)

67% (F1-Score) by Fine-tuned mBert-case

Sentence-Level Factuality Prediction (90%/10% 10-fold split)

88% (F1-Score) by Fine-tuned mBert-case

Citation

Vargas, F., Jaidka, K., Pardo, T.A.S., Benevenuto, F. (2023). Predicting Sentence-Level Factuality of News and Bias of Media Outlets. Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pp.1197--1206. Varna, Bulgaria. Association for Computational Linguistics (ACL).

Bibtex

@inproceedings{vargas-etal-2023-predicting,
    title = "Predicting Sentence-Level Factuality of News and Bias of Media Outlets",
    author = "Vargas, Francielle  and
      Jaidka, Kokil  and
      Pardo, Thiago  and
      Benevenuto, Fabr{\'\i}cio",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://aclanthology.org/2023.ranlp-1.127",
    pages = "1197--1206",
}