Datasets:
license: apache-2.0
task_categories:
- text-classification
language:
- pt
tags:
- fact-checking
- misinformation
- factuality
- NLP
- LLMs
- ML
pretty_name: FactNews
Evaluation Benchmark for Sentence-Level Factuality Prediciton in Portuguese
The FactNews consits of the first large sentence-level annotated corpus for factuality prediciton in Portuguese. It is composed of 6,191 sentences annotated according to factuality and media bias definitions proposed by AllSides. We use 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 entire media outlets.
Dataset Description
Proposed by : Francielle Vargas (https://franciellevargas.github.io/)
Funded by : Google
Language(s) (NLP): Portuguese
Dataset Sources
Repository: https://github.com/franciellevargas/FactNews
Demo: FACTual Fact-Checking: http://143.107.183.175:14582/
Paper
Predicting Sentence-Level Factuality of News and Bias of Media Outlets
Francielle Vargas, Kokil Jaidka, Thiago A.S. Pardo, Fabrício Benevenuto
Recent Advances in Natural Language Processing (RANLP 2023)
Varna, Bulgaria. https://aclanthology.org/2023.ranlp-1.127/