Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions
This work addresses bias assessment for professionals, organizations, and researchers relying on truthful evidence, though it appears incremental as an extension of an existing method.
The paper tackled the problem of assessing factual reporting and political bias in news sources by extending a reliability estimation method that models outlets and their web interactions, achieving significant performance improvements at the source media level and outperforming results on the CLEF 2023 CheckThat! Lab challenge in F1-score and MAE.
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.