媒体景观映射：通过网络互动预测事实报道和政治偏见

Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

October 23, 2024

作者: Dairazalia Sánchez-Cortés, Sergio Burdisso, Esaú Villatoro-Tello, Petr Motlicek

cs.AI

摘要

对于依赖真实证据进行信息收集和报道的专业人士、组织和研究人员来说，对新闻来源进行偏见评估至关重要。虽然某些偏见指标可以通过内容分析辨别出来，但诸如政治偏见和虚假新闻等描述词会带来更大的挑战。本文提出了对最近提出的新闻媒体可靠性估计方法进行扩展，重点是对媒体和它们的长期网络交互进行建模。具体而言，我们评估了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。我们的实验针对两个具有挑战性的偏见描述词，即事实报道和政治偏见，在源媒体级别展示了显著的性能改进。此外，我们在CLEF 2023 CheckThat! Lab挑战赛上验证了我们的方法，在F1分数和官方MAE指标上均超过了报告的结果。此外，我们通过发布了一个包含事实报道和政治偏见标签的最大注释新闻来源媒体数据集做出了贡献。我们的研究结果表明，基于新闻媒体源的超链接交互随时间的变化进行配置是可行的，可以提供媒体景观演变的整体视角。

English

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.