Analysis of Media Writing Style Bias through Text-Embedding Networks

Iain J. Cruickshank, Jessica Zhu, Nathaniel D. Bastian

With the rise of phenomena like `fake news' and the growth of heavily-biased media ecosystems, there has been increased attention on understanding and evaluating media bias. Of particular note in the evaluation of media bias is writing style bias, which includes lexical bias and framing bias. We propose a novel approach to evaluating writing style bias that utilizes natural language similarity estimation and a network-based representation of the shared content between articles to perform bias characterization. Our proposed method presents a new means of evaluating writing style bias that does not rely on human experts or knowledge of a media producer's publication procedures. The results of experimentation on real-world vaccine mandate data demonstrates the utility of the technique and how the standard bias labeling procedures of only having one bias label for a media producer is insufficient to truly characterize the bias of that media producer.

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