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What is Robust Fake News Detection Over Time about?

This study investigates the effects of time on the performance of news veracity classifiers, revealing that while classification accuracy degrades over time, it does so more slowly than anticipated due to the robustness of content-based features. The research also demonstrates that online learning can mitigate performance degradation and that static models are vulnerable to adversarial attacks, whereas online models can recover from such manipulations. Overall, the findings suggest that content-based method

Author

mohammedb220397cs

Language

EN