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arXiv · 1802.05762

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

Karthik Sheshadri·Chung-Wei Hang·Munindar Singh
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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.

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Karthik Sheshadri, Chung-Wei Hang, Munindar Singh. 2018-02-15. Framing Matters: Predicting Framing Changes and Legislation from Topic News Patterns. https://arxiv.org/abs/1802.05762

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