  News has traditionally been well researched, with studies ranging from
sentiment analysis to event detection and topic tracking. We extend the focus
to two surprisingly under-researched aspects of news: \emph{framing} and
\emph{predictive utility}. We demonstrate that framing influences public
opinion and behavior, and present a simple entropic algorithm to characterize
and detect framing changes. We introduce a dataset of news topics with framing
changes, harvested from manual surveys in previous research. Our approach
achieves an F-measure of $F_1=0.96$ on our data, whereas dynamic topic modeling
returns $F_1=0.1$. We also establish that news has \emph{predictive utility},
by showing that legislation in topics of current interest can be foreshadowed
and predicted from news patterns.
