Ringtail

Abstract

Social media nowcasting--using online user activity to describe 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 nowcasting systems to quickly become a standard tool among noncomputer scientists, yet it has largely remained a research topic. We believe a major obstacle to widespread adoption is the nowcasting feature selection problem. Typical nowcasting 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 RINGTALL 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 RINGTALL to quickly return a usable predictive nowcasting model.