Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News
CoRR(2024)
摘要
The mainstream media has much leeway in what it chooses to cover and how it
covers it. These choices have real-world consequences on what people know and
their subsequent behaviors. However, the lack of objective measures to evaluate
editorial choices makes research in this area particularly difficult. In this
paper, we argue that there are newsworthy topics where objective measures exist
in the form of supporting data and propose a computational framework to analyze
editorial choices in this setup. We focus on the economy because the reporting
of economic indicators presents us with a relatively easy way to determine both
the selection and framing of various publications. Their values provide a
ground truth of how the economy is doing relative to how the publications
choose to cover it. To do this, we define frame prediction as a set of
interdependent tasks. At the article level, we learn to identify the reported
stance towards the general state of the economy. Then, for every numerical
quantity reported in the article, we learn to identify whether it corresponds
to an economic indicator and whether it is being reported in a positive or
negative way. To perform our analysis, we track six American publishers and
each article that appeared in the top 10 slots of their landing page between
2015 and 2023.
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