2024
DOI: 10.3390/app14083532
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RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach

Jianqiao Lai,
Xinran Yang,
Wenyue Luo
et al.

Abstract: With the rapid development of the Internet and social media, false information, rumors, and misleading content have become pervasive, posing significant threats to public opinion and social stability, and even causing serious societal harm. This paper introduces a novel solution to address the challenges of fake news detection, presenting the “Rumor Large Language Models” (RumorLLM), a large language model finetuned with rumor writing styles and content. The key contributions include the development of RumorLL… Show more

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Cited by 6 publications
(1 citation statement)
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References 44 publications
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“…In each pair, both authors were considered of the same importance, with the same costs of misclassification. To further limit the number of influential factors, the data was prepared to obtain a balance of representation, in particular a balance of classes [37,38].…”
Section: Data Preparationmentioning
confidence: 99%
“…In each pair, both authors were considered of the same importance, with the same costs of misclassification. To further limit the number of influential factors, the data was prepared to obtain a balance of representation, in particular a balance of classes [37,38].…”
Section: Data Preparationmentioning
confidence: 99%