Detecting Framing Changes in Topical News

Changes in the framing of topical news are known to foreshadow significant public, legislative, and commercial events. Automated detection of framing changes is, therefore, an important problem, which existing research has not considered. Previous approaches are manual surveys that rely on human effort and are consequently limited in scope. This article systematizes the discovery of framing changes through a fully unsupervised computational method that seeks to isolate framing change trends over several years. We demonstrate our approach by isolating framing change periods that correlate with previously known framing changes. We have prepared a new data set, consisting of over 12 000 articles from seven news topics or
domains
, in which earlier surveys have found framing changes. Finally, our work highlights the predictive utility of framing change detection, by identifying two domains in which framing changes foreshadowed substantial legislative activity, or preceded judicial interest.