The COVID That Wasn’t: Counterfactual Journalism Using GPT

Sil Hamilton, Andrew Piper


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.
Anthology ID:
2022.latechclfl-1.11
Volume:
Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Stefania Degaetano, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
Venue:
LaTeCHCLfL
SIG:
SIGHUM
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
83–93
Language:
URL:
https://aclanthology.org/2022.latechclfl-1.11
DOI:
Bibkey:
Cite (ACL):
Sil Hamilton and Andrew Piper. 2022. The COVID That Wasn’t: Counterfactual Journalism Using GPT. In Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 83–93, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
The COVID That Wasn’t: Counterfactual Journalism Using GPT (Hamilton & Piper, LaTeCHCLfL 2022)
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PDF:
https://aclanthology.org/2022.latechclfl-1.11.pdf