Assessing Partisan Traits of News Text Attributions
Logan Martel, Edward Newell, Drew Margolin, Derek Ruths
TL;DR
This paper introduces a computational method to analyze and compare partisan traits in news attributions, focusing on statements about Hillary Clinton and Donald Trump, achieving high accuracy in candidate identification.
Contribution
It develops a model trained on annotated data to identify candidate attributions and explores partisan differences in news text attributions.
Findings
Model achieves over 88% accuracy in candidate identification.
Insights reveal partisan traits in news attributions.
Framework supports future research on media bias analysis.
Abstract
On the topic of journalistic integrity, the current state of accurate, impartial news reporting has garnered much debate in context to the 2016 US Presidential Election. In pursuit of computational evaluation of news text, the statements (attributions) ascribed by media outlets to sources provide a common category of evidence on which to operate. In this paper, we develop an approach to compare partisan traits of news text attributions and apply it to characterize differences in statements ascribed to candidate, Hilary Clinton, and incumbent President, Donald Trump. In doing so, we present a model trained on over 600 in-house annotated attributions to identify each candidate with accuracy > 88%. Finally, we discuss insights from its performance for future research.
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