metadata

license: apache-2.0
tags:
  - information-verification
  - fact-checking
  - fake-news-detection
pretty_name: >-
  Mapping the Media Landscape: Predicting Factual Reporting and Political Bias
  Through Web Interactions
size_categories:
  - 1K<n<10K

Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

Dataset introduced in the paper "Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions" published in the CLEF 2024 main conference.

The dataset consists of a collections of 4K new media domains names with political bias and factual reporting labels.

Columns of the dataset:

source: domain name
bias: the political bias label. Values: "left", "left-center", "neutral", "right-center", "right".
factual_reporting: the factual reporting label. Values: "low", "mixed", "high".

Github repo released along with the paper here.

Load Training Dataset

from datasets import load_dataset

dataset = load_dataset('sergioburdisso/news_media_bias_and_factuality')

print(dataset)

Output:

DatasetDict({
    train: Dataset({
        features: ['source', 'bias', 'factual_reporting'],
        num_rows: 3920
    })
})

Citation

Springer Paper: here.

@inproceedings{sanchez2024mapping,
  title={Mapping the media landscape: predicting factual reporting and political bias through web interactions},
  author={S{\'a}nchez-Cort{\'e}s, Dairazalia and Burdisso, Sergio and Villatoro-Tello, Esa{\'u} and Motlicek, Petr},
  booktitle={International Conference of the Cross-Language Evaluation Forum for European Languages},
  pages={127--138},
  year={2024},
  organization={Springer}
}

Datasets:

sergioburdisso
/

news_media_bias_and_factuality

Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

Load Training Dataset

Citation

License