The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large. What's more, we find evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), depression, bipolar disorder, and seasonal affective disorder (SAD). We also discuss plans for an upcoming hackathon (in Baltimore, Nov 7-9) and shared task using the data used from these investigations. We will also share the data with the computational linguistics and clinical psychology community as a resource (subject to some reasonable privacy protections and agreements), hoping to fuel future work and collective purpose. We intend for these proof-of-concept results, experiments, and explorations to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.