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Framing Matters : Predicting Framing Changes and Legislation from Topic News Cycles
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sheshadri, Karthik Hang, Chung-Wei Singh, Munindar P. |
| Copyright Year | 2018 |
| 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: 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 F1 = 0.96 on our data, whereas dynamic topic modeling returns F1 = 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://export.arxiv.org/pdf/1802.05762 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Notice |
