Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features
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Computer Science > Computation and Language
arXiv:2606.04199 (cs)
[Submitted on 2 Jun 2026]
Title:Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features
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Abstract:The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies. Most existing detection models are trained and evaluated under a single generation setting, leaving their ability to generalize across unseen prompts unclear. In this study, we investigate cross-prompt generalization in fake news detection using three datasets of AI-generated articles produced under distinct prompts, combined with real news articles. We extract interpretable linguistic features capturing lexical diversity, readability, and emotion-based characteristics and evaluate a random forest classifier under a cross-prompt framework, where models trained on one prompt are tested on another. Across all six train-test combinations, performance remains consistently high, with AUC values ranging from 0.988 to 1.000. Analysis of feature distributions shows that AI-generated text exhibits increased lexical diversity, reduced readability, and substantially lower emotional intensity compared to the overall dataset, with variations across prompts. Despite these distributional shifts, the classifier maintains strong performance, indicating that these features capture stable properties of AI-generated text that generalize across prompting strategies. These findings suggest that feature-based approaches can provide robust detection of AI-generated fake news under prompt variability.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.04199 [cs.CL] |
| (or arXiv:2606.04199v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04199
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features, by Aya Vera-Jimenez and 3 other authors
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