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 F1=0.96 on our data, whereas dynamic topic modeling returns F1=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.
@article{arxiv.1802.05762,
title = {Framing Matters: Predicting Framing Changes and Legislation from Topic News Patterns},
author = {Karthik Sheshadri and Chung-Wei Hang and Munindar Singh},
journal= {arXiv preprint arXiv:1802.05762},
year = {2018}
}