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.

翻译：对于依赖真实证据进行信息收集与报道的专业人士、组织及研究者而言，新闻来源的偏见评估至关重要。虽然某些偏见指标可通过内容分析识别，但政治偏见和虚假新闻等描述性指标则更具挑战性。本文提出对近期一种新闻媒体可靠性评估方法的扩展，该方法专注于对新闻机构及其纵向网络互动进行建模。具体而言，我们在一个大型新闻媒体超链接图上评估了四种强化学习策略的分类性能。针对事实报道与政治偏见这两个具有挑战性的描述指标，我们的实验在新闻源媒体层面显示出显著的性能提升。此外，我们在CLEF 2023 CheckThat! Lab挑战任务上验证了所提方法，在F1分数和官方MAE指标上均优于已报道结果。同时，我们发布了目前最大规模的新闻源媒体标注数据集，该数据集包含事实报道与政治偏见的分类标签。研究结果表明，基于新闻媒体随时间演化的超链接互动对其进行画像具有可行性，这为动态变化的媒体格局提供了宏观视角。