Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage

TL;DR

This paper advocates for integrating machine learning, journalism, and statistics to analyze international news data, aiming to improve understanding and transparency of global issues like climate change and migration.

Contribution

It introduces a multidisciplinary approach combining machine learning and journalism studies to analyze segmented international news data, exemplified through the TTIP case study.

Findings

Highlights the importance of cross-disciplinary methods for global news analysis

Proposes a framework to bridge language and segmentation barriers in international news

Demonstrates potential for improved transparency in international debates

Abstract

Migration crisis, climate change or tax havens: Global challenges need global solutions. But agreeing on a joint approach is difficult without a common ground for discussion. Public spheres are highly segmented because news are mainly produced and received on a national level. Gain- ing a global view on international debates about important issues is hindered by the enormous quantity of news and by language barriers. Media analysis usually focuses only on qualitative re- search. In this position statement, we argue that it is imperative to pool methods from machine learning, journalism studies and statistics to help bridging the segmented data of the international public sphere, using the Transatlantic Trade and Investment Partnership (TTIP) as a case study.