Datasets:
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",
}