Luca Aiello, ITU (NERDS): Towards Health Surveillance with Social Media

In today’s heavily interconnected world, health crises develop rapidly and spread afar. Health crises burden not only people’s biological health but also their psychological and social spheres. By monitoring the population’s behavior and health conditions, public health bodies can plan prevention campaigns, promptly deploy targeted interventions, and adapt to people’s responses. We contributed to this stream of research by developing a Deep Learning tool for Natural Language Processing that extracts mentions of virtually any medical condition from unstructured social media text. We applied the tool to Reddit and Twitter posts, analyzed the clusters of the two resulting co-occurrence networks of conditions, and discovered that they correspond to well-defined categories of medical conditions. This resulted in the creation of a taxonomy of medical conditions automatically derived from online discussions. We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22. Based on the mentions of our taxonomy’s sub-categories on Reddit posts geo-referenced in the U.S., we were then able to compute disease-specific health scores which correlate with officially reported prevalences of 18 conditions.