Social media nowcasting—using online user activity to de- scribe real-world phenomena—is an active area of research to supplement more traditional and costly data collection methods such as phone surveys. Given the potential impact of such research, we would expect general-purpose nowcast- ing systems to quickly become a standard tool among non- computer scientists, yet it has largely remained a research topic. We believe a major obstacle to widespread adoption is the nowcasting feature selection problem. Typical now- casting systems require the user to choose a handful of social media objects from a pool of billions of potential candidates, which can be a time-consuming and error-prone process. We have built Ringtail, a nowcasting system that helps the user by automatically suggesting high-quality signals. We demonstrate that Ringtail can make nowcasting easier by suggesting relevant features for a range of topics. The user provides just a short topic query (e.g., unemployment) and a small conventional dataset in order for Ringtail to quickly return a usable predictive nowcasting model.