Ringtail: Feature Selection for Easier Nowcasting.

Antenucci, Dolan, Michael J. Cafarella, Margaret C. Levenstein, Christopher Ré, and Matthew Shapiro. "Ringtail: Feature Selection for Easier Nowcasting." WebDB (2013): 49-54, available at http://www.cs.stanford.edu/people/chrismre/papers/webdb_ringtail.pdf.
In recent years, social media “nowcasting”—the use of on- line user activity to predict various ongoing real-world social phenomena—has become a popular research topic; yet, this popularity has not led to widespread actual practice. We be- lieve a major obstacle to widespread adoption is the feature selection problem. Typical nowcasting systems require the user to choose a set of relevant social media objects, which is difficult, time-consuming, and can imply a statistical back- ground that users may not have. We propose Ringtail, which helps the user choose rele- vant social media signals. It takes a single user input string (e.g., unemployment) and yields a number of relevant signals the user can use to build a nowcasting model. We evaluate Ringtail on six different topics using a corpus of almost 6 billion tweets, showing that features chosen by Ringtail in a wholly-automated way are better or as good as those from a human and substantially better if Ringtail receives some human assistance. In all cases, Ringtail reduces the burden on the user.