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metadata
dataset_info:
  - config_name: bias_prediction
    features:
      - name: file
        dtype: string
      - name: id_sente
        dtype: string
      - name: id_article
        dtype: string
      - name: domain
        dtype: string
      - name: year
        dtype: string
      - name: sentences
        dtype: string
      - name: label
        dtype: int64
      - name: label_text
        dtype: string
    splits:
      - name: train
        num_bytes: 163041
        num_examples: 738
      - name: full_train
        num_bytes: 951010
        num_examples: 4403
      - name: test
        num_bytes: 384327
        num_examples: 1788
    download_size: 718605
    dataset_size: 1498378
  - config_name: factuality_prediction
    features:
      - name: file
        dtype: string
      - name: id_sente
        dtype: string
      - name: id_article
        dtype: string
      - name: domain
        dtype: string
      - name: year
        dtype: string
      - name: sentences
        dtype: string
      - name: label
        dtype: int64
      - name: label_text
        dtype: string
    splits:
      - name: train
        num_bytes: 606722
        num_examples: 2826
      - name: full_train
        num_bytes: 944929
        num_examples: 4403
      - name: test
        num_bytes: 381863
        num_examples: 1788
    download_size: 927856
    dataset_size: 1933514
  - config_name: original
    features:
      - name: file
        dtype: string
      - name: id_sente
        dtype: string
      - name: id_article
        dtype: string
      - name: domain
        dtype: string
      - name: year
        dtype: string
      - name: sentences
        dtype: string
      - name: classe
        dtype: int64
      - name: label_text
        dtype: string
    splits:
      - name: train
        num_bytes: 1317047
        num_examples: 6191
    download_size: 516651
    dataset_size: 1317047
configs:
  - config_name: bias_prediction
    data_files:
      - split: train
        path: bias_prediction/train-*
      - split: full_train
        path: bias_prediction/full_train-*
      - split: test
        path: bias_prediction/test-*
  - config_name: factuality_prediction
    data_files:
      - split: train
        path: factuality_prediction/train-*
      - split: full_train
        path: factuality_prediction/full_train-*
      - split: test
        path: factuality_prediction/test-*
  - config_name: original
    data_files:
      - split: train
        path: original/train-*
license: unknown
task_categories:
  - text-classification
language:
  - pt
  - por
pretty_name: FactNews
size_categories:
  - 1K<n<10K
multilinguality:
  - monolingual
language_creators:
  - found
annotations_creators:
  - expert-generated
tags:
  - subjectivity
  - mediabias
  - media-bias

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",
}

Dataset Description