Effect of climate and seasonality on depressed mood among twitter users

Location-based social media provide an enormous stream of data about humans' life and behavior. With geospatial methods, those data can offer rich insights into public health. In this research, we study the effect of climate and seasonality on the prevalence of depression in Twitter users in the U.S. Text mining and geospatial methods are used to detect tweets related to depression and their spatiotemporal patterns at the scale of Metropolitan Statistical Area. We find the relationship between depression rates, climate risk factors and seasonality are varied and geographically localized. The same climate measure may have opposite association with depression rates at different places. Relative humidity, temperature, sea level pressure, precipitation, snowfall, weed speed, globe solar radiation, and length of day all contribute to the geographic variations of depression rates. A conceptual compact map is designed to visualize scattered geographic phenomena in a large area. We also propose a three-stage framework that semi-automatically detects and analyzes geographically distributed health issues using location-based social media data.