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This study investigates using unreliable narrator theory to improve open-weight models' ability to explain satirical news, offering a cost-effective alternative to frontier models for handling cultural context differences between US and Australian sources. By establishing a novel dataset and benchmark, the work provides practical tools for refining NLP systems in misinformation detection and content interpretation tasks.
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News
arXiv:2610.12863v1 Announce Type: new Abstract: Satirical news is a unique form of misinformation: it is intended to be humorous by the original publication source but can be misinterpreted. Explanation of satirical news articles is challenging because recent and local context needs to be incorporated. This is a pilot study that explores explanation generation for satirical news articles from Australia and compares them alongside those from USA. The paper demonstrates that framing explanations on unreliable narrator theory on open-weights models can be a feasible, cost-effective alternative to frontier models. We find that models identify the satirical tropes more accurately on the American source than the Australian one. This work results in a novel dataset and an evaluation benchmark for satirical explanation.
Abstract:Satirical news is a unique form of misinformation: it is intended to be humorous by the original publication source but can be misinterpreted. Explanation of satirical news articles is challenging because recent and local context needs to be incorporated. This is a pilot study that explores explanation generation for satirical news articles from Australia and compares them alongside those from USA. The paper demonstrates that framing explanations on unreliable narrator theory on open-weights models can be a feasible, cost-effective alternative to frontier models. We find that models identify the satirical tropes more accurately on the American source than the Australian one. This work results in a novel dataset and an evaluation benchmark for satirical explanation.
Computation and Language (cs.CL)
arXiv:2610.12863 [cs.CL]
(or arXiv:2610.12863v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.12863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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