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

arXiv: https://arxiv.org/abs/1606.05110 · HTML: https://mlsanity.com/paper/1606.05110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Classification of news spreading barriers](https://mlsanity.com/paper/2304.08167/) (2023)

## Abstract (arXiv)

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.