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Conference object . 2026
License: CC BY
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Article . 2026
License: CC BY
Data sources: Datacite
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Article . 2026
License: CC BY
Data sources: Datacite
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Rewriting the Past for the Present: A Computational Study of Sitcom Presentism using Fine-Tuned Language Models

Authors: Almasi, Mehrdad; Karatas, Tugce;

Rewriting the Past for the Present: A Computational Study of Sitcom Presentism using Fine-Tuned Language Models

Abstract

This paper explores sitcom presentism by using fine-tuned language models to rewrite 1990s Seinfeld plots as plausible 2025 versions while preserving their core comedic beats and character dynamics. Through a three-stage computational pipeline, the study shows that the model can produce culturally updated plots with strong semantic continuity, low lexical overlap, and high diversity, framing presentism as adaptation rather than simple repetition.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average