Datasets:

eduagarcia
/

FactNews

	---
	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

	- 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)