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arXiv · 2210.06644

The COVID That Wasn't: Counterfactual Journalism Using GPT

Sil Hamilton·Andrew Piper
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Abstract

In this paper, we explore the use of large language models to assess human interpretations of real world events. To do so, we use a language model trained prior to 2020 to artificially generate news articles concerning COVID-19 given the headlines of actual articles written during the pandemic. We then compare stylistic qualities of our artificially generated corpus with a news corpus, in this case 5,082 articles produced by CBC News between January 23 and May 5, 2020. We find our artificially generated articles exhibits a considerably more negative attitude towards COVID and a significantly lower reliance on geopolitical framing. Our methods and results hold importance for researchers seeking to simulate large scale cultural processes via recent breakthroughs in text generation.

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Sil Hamilton, Andrew Piper. 2022-10-13. The COVID That Wasn't: Counterfactual Journalism Using GPT. https://arxiv.org/abs/2210.06644

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