媒體景觀的映射：透過網絡互動預測事實報導和政治偏見

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

對於依賴真實證據進行資訊收集和報導的專業人士、組織和研究人員來說，評估新聞來源的偏見至關重要。雖然某些偏見指標可以從內容分析中辨識出來，但政治偏見和假新聞等描述符帶來更大的挑戰。在本文中，我們提出了一種擴展的新聞媒體可靠性估計方法，該方法專注於對媒體和其長期網絡互動進行建模。具體來說，我們評估了四種強化學習策略在一個大型新聞媒體超鏈接圖上的分類性能。我們的實驗針對兩個具有挑戰性的偏見描述符，即事實報導和政治偏見，顯示出在來源媒體層面上顯著的性能改進。此外，我們在CLEF 2023 CheckThat！實驗室挑戰中驗證了我們的方法，在F1分數和官方MAE指標方面超越了報告的結果。此外，我們通過釋放了最大的帶有事實報導和政治偏見標籤的新聞來源媒體注釋數據集來做出貢獻。我們的研究結果表明，基於媒體超鏈接互動隨時間的變化來對新聞媒體來源進行輪廓塑造是可行的，提供了對不斷演變的媒體格局的全貌。

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.