  The sequence of documents produced by any given author varies in style and
content, but some documents are more typical or representative of the source
than others. We quantify the extent to which a given short text is
characteristic of a specific person, using a dataset of tweets from fifteen
celebrities. Such analysis is useful for generating excerpts of high-volume
Twitter profiles, and understanding how representativeness relates to tweet
popularity. We first consider the related task of binary author detection (is x
the author of text T?), and report a test accuracy of 90.37% for the best of
five approaches to this problem. We then use these models to compute
characterization scores among all of an author's texts. A user study shows
human evaluators agree with our characterization model for all 15 celebrities
in our dataset, each with p-value < 0.05. We use these classifiers to show
surprisingly strong correlations between characterization scores and the
popularity of the associated texts. Indeed, we demonstrate a statistically
significant correlation between this score and tweet popularity
(likes/replies/retweets) for 13 of the 15 celebrities in our study.
