Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features

This study investigates cross-prompt generalization in detecting AI-generated fake news using interpretable linguistic features like lexical diversity and readability. A random forest classifier achieved consistently high performance (AUC 0.988-1.000) across various train-test combinations, demonstrating robustness against different prompting strategies.