Mapping the Media Landscape: Predicting Factual Reporting and Political
  Bias Through Web Interactions
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.
6719f2f4defdd700b13520a4
Dairazalia Sánchez-Cortés
6719f2f4defdd700b13520a5
Sergio Burdisso
claimed_verified
2024-10-24T07:15:17.790Z
61d626d9e3a234cfcb617dca
https://cdn-avatars.huggingface.co/v1/production/uploads/61d626d9e3a234cfcb617dca/kJD3oxckDgiaEnYwGth6p.jpeg
Sergio Burdisso
user
sergioburdisso
6719f2f4defdd700b13520a6
Esaú Villatoro-Tello
admin_assigned
2024-10-28T15:49:51.940Z
661fed2e9e3858af5d7037dc
/avatars/07b8256344e093e179ebf9e724ae5075.svg
Esau Villatoro-Tello
user
evillatoro
6719f2f4defdd700b13520a7
Petr Motlicek
Mapping the Media Landscape: Predicting Factual Reporting and Political
  Bias Through Web Interactions
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.
Evaluating news media reliability through reinforcement learning methods on hyperlink interactions achieves significant performance improvement for factual reporting and political bias assessment.
reinforcement learning news media reliability longitudinal web interactions classification performance CLEF 2023 CheckThat! Lab factual reporting political bias F1-score MAE metric