Framing Matters: Predicting Framing Changes and Legislation from Topic News Patterns

TL;DR

This paper introduces a simple entropic algorithm to detect framing changes in news, demonstrates the influence of framing on public opinion, and shows that news patterns can predict future legislation.

Contribution

It presents a novel entropic method for framing change detection and establishes the predictive utility of news patterns for legislation forecasting.

Findings

Framing detection achieves an F1 score of 0.96.

Dynamic topic modeling yields an F1 score of 0.1.

News patterns can foreshadow legislation.

Abstract

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.