%0 Generic %1 erdmann2016machine %A Erdmann, Elena %A Boczek, Karin %A Koppers, Lars %A von Nordheim, Gerret %A Pölitz, Christian %A Molina, Alejandro %A Morik, Katharina %A Müller, Henrik %A Rahnenführer, Jörg %A Kersting, Kristian %D 2016 %K ml proposal tau %T Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage %U http://arxiv.org/abs/1606.05110 %X 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.

@misc{erdmann2016machine, 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.}, added-at = {2016-11-29T12:04:35.000+0100}, author = {Erdmann, Elena and Boczek, Karin and Koppers, Lars and von Nordheim, Gerret and Pölitz, Christian and Molina, Alejandro and Morik, Katharina and Müller, Henrik and Rahnenführer, Jörg and Kersting, Kristian}, biburl = {https://www.bibsonomy.org/bibtex/2bab68e6033faa25f82bc5a4469d158d5/thoni}, description = {[1606.05110] Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage}, interhash = {9f3e5f80e0e2438c6b376efe66746cf9}, intrahash = {bab68e6033faa25f82bc5a4469d158d5}, keywords = {ml proposal tau}, note = {cite arxiv:1606.05110Comment: presented at 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, NY}, timestamp = {2016-11-29T12:04:35.000+0100}, title = {Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage}, url = {http://arxiv.org/abs/1606.05110}, year = 2016 }