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Researchers can detect bots in online surveys using a new unsupervised algorithm. Performance is best with more items, categories, and varied item difficulty, ensuring data quality.

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Area of Science:

  • Social Sciences
  • Psychometrics
  • Data Science

Background:

  • Online surveys are widely used in social sciences.
  • Bot prevalence threatens data quality and research integrity.
  • Detecting bots is crucial when preventative measures fail.

Purpose of the Study:

  • To evaluate a new unsupervised algorithm for detecting bots in survey data.
  • To understand factors influencing the algorithm's classification accuracy.
  • To identify conditions for reliable bot detection.

Main Methods:

  • Utilized simulations with hypothetical human responses from item response theory models.
  • Contaminated real human data with simulated bots across 35 datasets.
  • Employed a permutation test assuming item exchangeability for bots but not humans.

Main Results:

  • Algorithm accuracy is sensitive to item properties, number of items, latent factors, and factor correlations.
  • High classification accuracy (around 95%+) achieved under optimal conditions.
  • Lower accuracy observed under certain conditions, highlighting performance variability.

Conclusions:

  • The model-agnostic, unsupervised algorithm shows promise for bot detection.
  • Classification accuracy is enhanced by more items, more response categories, and item difficulty variation.
  • Researchers must consider these factors to ensure reliable bot detection and maintain data integrity.