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The problem of detecting scientific fraud using machine learning was recentlyintroduced, with initial, positive results from a model taking into accountvarious general indicators. The results seem to suggest that writing style ispredictive of scientific fraud. We revisit these initial experiments, and showthat the leave-one-out testing procedure they used likely leads to a slightover-estimate of the predictability, but also that simple models can outperformtheir proposed model by some margin. We go on to explore more abstractlinguistic features, such as linguistic complexity and discourse structure,only to obtain negative results. Upon analyzing our models, we do see someinteresting patterns, though: Scientific fraud, for examples, contains lesscomparison, as well as different types of hedging and ways of presentinglogical reasoning.

Is writing style predictive of scientific fraud?