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.

本研究解决了新闻源偏见评估中的一项重要问题，即如何基于长期网络互动来评估和分类媒体的事实报道和政治偏见。提出了一种扩展的新闻媒体可靠性估计方法，通过对四种强化学习策略在大型新闻媒体超链接图上的分类性能进行评估，显示出显著的提升。此外，研究结果表明，通过时间推移分析媒体链接互动，能够有效描绘演变中的媒体环境。

映射媒体环境：通过网络互动预测事实报道和政治偏见