  Predicting the quality of a text document is a critical task when presented
with the problem of measuring the performance of a document before its release.
In this work, we evaluate various features including those extracted from the
text content (textual) and those describing higher-level characteristics of the
text (meta) features that are not directly available from the text, and show
how these features inform prediction of document quality in different ways.
Moreover, we also compare our methods on both social user-generated data such
as tweets, and scholarly user-generated data such as academic articles, showing
how the same features differently influence prediction of quality across these
disparate domains.
