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

02/15/2018

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by   Karthik Sheshadri, et al.

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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: framing and 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 predictive utility, by showing that legislation in topics of current interest can be foreshadowed and predicted from news patterns.