|
--- |
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dataset_info: |
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- config_name: bias_prediction |
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features: |
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- name: file |
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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 |
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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 |
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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 |
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configs: |
|
- config_name: bias_prediction |
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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 |
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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. |
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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: |
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- Media 1: Folha de São Paulo |
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- Media 2: Estadão |
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- 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) |
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- 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 |
|
|
|
- **Homepage:** https://github.com/franciellevargas/FactNews |
|
- **Paper:** [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://aclanthology.org/2023.ranlp-1.127) |