The impact of health-related news in today’s society is increasing as is the awareness of the globalization of the worlds’ habits and threats, and the impact on the continuous pursuit of a better quality of life. The risk of news media bias and the consequences it might have in the population is of great concern for public health, as are the available resources to identify the bias and further explore the news stories. In this paper we discuss several aspects, angles and perspectives on news media bias in the health domain, with a particular focus on digital epidemiology. We also present decision support tools developed to support decision makers in these explorations in the context of the MIDAS project, leveraging Big Data analytics to support decision making in public health. The presented resources provide health professionals with a global perspective on the worldwide news coverage of monitored health topics (such as, e.g., infectious diseases, mental health or childhood obesity), together with a workflow of tools allowing them to explore potential bias. Moreover, we discuss the specific challenges of news bias in the health domain, analyzing some typical examples, and using the Event Registry technology to further explore them. The exploration potential of the latter, in the health domain, is enhanced with the integration of an automated classifier based on MeSH Headings that allows researchers to explore the news using a similar workflow to that of exploring biomedical research in PubMed.