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

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.Comment: presented at 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, N

Beliefs about the Minds of Others Influence How We Process Sensory Information

Attending where others gaze is one of the most fundamental mechanisms of social cognition. The present study is the first to examine the impact of the attribution of mind to others on gaze-guided attentional orienting and its ERP correlates. Using a paradigm in which attention was guided to a location by the gaze of a centrally presented face, we manipulated participants' beliefs about the gazer: gaze behavior was believed to result either from operations of a mind or from a machine. In Experiment 1, beliefs were manipulated by cue identity (human or robot), while in Experiment 2, cue identity (robot) remained identical across conditions and beliefs were manipulated solely via instruction, which was irrelevant to the task. ERP results and behavior showed that participants' attention was guided by gaze only when gaze was believed to be controlled by a human. Specifically, the P1 was more enhanced for validly, relative to invalidly, cued targets only when participants believed the gaze behavior was the result of a mind, rather than of a machine. This shows that sensory gain control can be influenced by higher-order (task-irrelevant) beliefs about the observed scene. We propose a new interdisciplinary model of social attention, which integrates ideas from cognitive and social neuroscience, as well as philosophy in order to provide a framework for understanding a crucial aspect of how humans' beliefs about the observed scene influence sensory processing