Administering Likert-type questionnaires to online samples risks contamination of the data by malicious computer-generated random responses, also known as bots. Although nonresponsivity indices (NRIs) such as person-total correlations or Mahalanobis distance have shown great promise to detect bots, universal cutoff values are elusive. An initial calibration sample constructed via stratified sampling of bots and humans—real or simulated under a measurement model—has been used to empirically choose cutoffs with a high nominal specificity. However, a high-specificity cutoff is less accurate when the target sample has a high contamination rate. In the present article, we propose the supervised classes, unsupervised mixing proportions (SCUMP) algorithm that chooses a cutoff to maximize accuracy. SCUMP uses a Gaussian mixture model to estimate, unsupervised, the contamination rate in the sample of interest. A simulation study found that, in the absence of model misspecification on the bots, our cutoffs maintained accuracy across varying contamination rates.