title: Mapping the Media Landscape: Predicting Factual Reporting and Political
    Bias Through Web Interactions
summary: 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.
paper_authors._id: 6719f2f4defdd700b13520a4
paper_authors.name: Dairazalia Sánchez-Cortés
paper_authors._id: 6719f2f4defdd700b13520a5
paper_authors.name: Sergio Burdisso
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-24T07:15:17.790Z
paper_authors._id: 61d626d9e3a234cfcb617dca
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61d626d9e3a234cfcb617dca/kJD3oxckDgiaEnYwGth6p.jpeg
paper_authors.fullname: Sergio Burdisso
paper_authors.type: user
paper_authors.user: sergioburdisso
paper_authors._id: 6719f2f4defdd700b13520a6
paper_authors.name: Esaú Villatoro-Tello
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:49:51.940Z
paper_authors._id: 661fed2e9e3858af5d7037dc
paper_authors.avatarUrl: /avatars/07b8256344e093e179ebf9e724ae5075.svg
paper_authors.fullname: Esau Villatoro-Tello
paper_authors.type: user
paper_authors.user: evillatoro
paper_authors._id: 6719f2f4defdd700b13520a7
paper_authors.name: Petr Motlicek
paper_title: Mapping the Media Landscape: Predicting Factual Reporting and Political
    Bias Through Web Interactions
paper_summary: 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.
paper_ai_keywords: reinforcement learning hyperlink graph classification performance factual reporting political bias CLEF 2023 CheckThat! Lab challenge F1-score official MAE metric annotated dataset