Mapping the Media Landscape: Predicting Factual Reporting and Political
  Bias Through Web Interactions
Authors: name: Dairazalia Sánchez-Cortés, name: Sergio Burdisso, name: Esaú Villatoro-Tello, name: Petr Motlicek

Bias assessment of news sources is paramount for professionals,
organizations, and researchers who rely on truthful evidence for information
gathering and reporting. While certain bias indicators are discernible from
content analysis, descriptors like political bias and fake news pose greater
challenges. In this paper, we propose an extension to a recently presented news
media reliability estimation method that focuses on modeling outlets and their
longitudinal web interactions. Concretely, we assess the classification
performance of four reinforcement learning strategies on a large news media
hyperlink graph. Our experiments, targeting two challenging bias descriptors,
factual reporting and political bias, showed a significant performance
improvement at the source media level. Additionally, we validate our methods on
the CLEF 2023 CheckThat! Lab challenge, outperforming the reported results in
both, F1-score and the official MAE metric. Furthermore, we contribute by
releasing the largest annotated dataset of news source media, categorized with
factual reporting and political bias labels. Our findings suggest that
profiling news media sources based on their hyperlink interactions over time is
feasible, offering a bird's-eye view of evolving media landscapes.