Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage
This addresses the challenge for journalists and researchers in gaining a global view on issues like migration or climate change, but it is incremental as it builds on existing methods.
The paper tackles the problem of segmented international news coverage by proposing a multidisciplinary approach to analyze global debates, using the Transatlantic Trade and Investment Partnership (TTIP) as a case study, but does not report concrete results or numbers.
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.