arXiv:2512.24000v3 Announce Type: replace Abstract: Distinguishing fake or untrue news from satire or humor poses a unique challenge due to their overlapping linguistic features and divergent intent. This study develops WISE (Web Information Satire and Fakeness Evaluation) framework which benchmarks eight lightweight transformer models alongside two baseline models on a balanced dataset of 20,000 samples from Fakeddit, annotated as either fake news or satire. Using stratified 5-fold cross-validation, we evaluate models across comprehensive metrics including accuracy, precision, recall, F1-scor
The proliferation of generative AI tools makes the distinction between authentic, satiric, and deliberately misleading content increasingly difficult, necessitating new methods for classification.
The ability to accurately differentiate fake news from satire is crucial for maintaining information integrity and mitigating the societal impacts of misinformation in the age of advanced AI.
This research introduces WISE, a new framework that could improve the accuracy and efficiency of identifying and classifying web content, particularly in discerning between satire and fake news.
- · Fact-checking organizations
- · Social media platforms
- · AI content moderation developers
- · Digital journalism
- · Misinformation purveyors
- · Foreign influence operations
Improved automated tools for content classification could enhance the speed and accuracy of identifying deceptive information online.
Social platforms might adopt similar frameworks to implement more nuanced content policies, potentially reducing unintentional censorship of satire.
A more reliable distinction between satire and fake news could lead to greater public trust in online information and reduced polarization stemming from misattributed content.
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Read at arXiv cs.CL