Article

Supervised classes, unsupervised mixing proportions: Detection of bots in a Likert-type questionnaire

2022-03-15

Abstract excerpt

<p>Administering Likert-type questionnaires to online samples risks contamination of the data by malicious computer-generated random responses, i.e., 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-...

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Literature Corpus work: 04861fa8-bc15-58bf-b709-9dad9831c0e4
DOI: 10.31234/osf.io/7ezx2

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