TY  - RPRT
T1  - Using Social Media to Measure Labor Market Flows
Y1  - 2014
A1  - Antenucci, Dolan
A1  - Cafarella, Michael J
A1  - Levenstein, Margaret C.
A1  - Ré, Christopher
A1  - Shapiro, Matthew
UR  - http://www-personal.umich.edu/~shapiro/papers/LaborFlowsSocialMedia.pdf
ER  - 

TY  - JOUR
T1  - Ringtail: a generalized nowcasting system.
JF  - WebDB
Y1  - 2013
A1  - Antenucci, Dolan
A1  - Li, Erdong
A1  - Liu, Shaobo
A1  - Zhang, Bochun
A1  - Cafarella, Michael J
A1  - Ré, Christopher
AB  - 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.
VL  - 6
UR  - http://cs.stanford.edu/people/chrismre/papers/Ringtail-VLDB-demo.pdf
ER  - 

TY  - JOUR
T1  - Ringtail: Feature Selection for Easier Nowcasting.
JF  - WebDB
Y1  - 2013
A1  - Antenucci, Dolan
A1  - Cafarella, Michael J
A1  - Levenstein, Margaret C.
A1  - Ré, Christopher
A1  - Shapiro, Matthew
AB  - 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.
UR  - http://www.cs.stanford.edu/people/chrismre/papers/webdb_ringtail.pdf
ER  -