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Online health information is widely reported by individuals in social media, chat rooms, discussion boards and also by the news media. These informal sources of evidence about our individual health, attitudes and behaviours are driving the development of new techniques for studying human health in areas ranging from real-time disease surveillance, to understanding mental illness, to providing evidence for new applications of drugs. Informal patient data on the Web is increasing, accessible, low cost and seems likely to cover a greatly expanded population compared to traditional survey methods. However in order to understand and integrate this data researchers in Natural Language Processing (NLP) must grapple with theoretical, practical and ethical challenges. For example: How can machines achieve fine-grained analysis and understanding of laymen's health language? How does online health report data complement traditional survey data? How can we integrate online health data with other data sources such as databases and ontologies? What benefit could there be in a longitudinal analysis of an individual's online health reports over a period of time? Our talk will illustrate answers to these questions in the light of our recent attempts to harness social and news media as a new type of signal for understanding human health.