  Nowcasting based on social media text promises to provide unobtrusive and
near real-time predictions of community-level outcomes. These outcomes are
typically regarding people, but the data is often aggregated without regard to
users in the Twitter populations of each community. This paper describes a
simple yet effective method for building community-level models using Twitter
language aggregated by user. Results on four different U.S. county-level tasks,
spanning demographic, health, and psychological outcomes show large and
consistent improvements in prediction accuracies (e.g. from Pearson r=.73 to
.82 for median income prediction or r=.37 to .47 for life satisfaction
prediction) over the standard approach of aggregating all tweets. We make our
aggregated and anonymized community-level data, derived from 37 billion tweets
-- over 1 billion of which were mapped to counties, available for research.
