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Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys.

Zachary Joseph Roman1, Holger Brandt2, Jason Michael Miller3

  • 1Department of Psychology, University of Zurich, Zürich, Switzerland.

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This study introduces a Bayesian latent class model to identify bots on online survey platforms like Amazon Mechanical Turk (MTurk). This method effectively separates bot data from human responses, ensuring more accurate behavioral science research.

Keywords:
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Area of Science:

  • Behavioral Science
  • Computational Social Science
  • Data Science

Background:

  • Online survey platforms, such as Amazon Mechanical Turk (MTurk), are widely used by behavioral scientists.
  • A significant challenge is the presence of bots completing surveys for financial gain, which compromises data integrity.
  • Identifying and removing bot-generated data is crucial for valid research conclusions.

Purpose of the Study:

  • To present a Bayesian latent class joint modeling approach for identifying bots in online survey data.
  • To simultaneously estimate models of interest while distinguishing bot responses from genuine human participants.
  • To offer a flexible and empirically sound method for data cleaning in crowdsourced research.

Main Methods:

  • Development and application of a Bayesian latent class joint modeling technique.
  • Simulation studies to evaluate model performance across various scenarios (sample size, bot proportion, complexity).
  • Illustration of the model using an empirical political ideation survey dataset containing known bots.

Main Results:

  • Ignoring bots leads to severe parameter bias in data analysis.
  • The proposed Bayesian latent class model yields unbiased estimates, effectively controlling for bot-induced bias.
  • The model successfully differentiates bot response patterns from human responses aligned with item content.

Conclusions:

  • The Bayesian latent class joint modeling approach is a robust tool for detecting bots in online survey data.
  • Implementing this method enhances the validity of findings from platforms like MTurk.
  • Future data collection via online platforms should incorporate such advanced methods to ensure data quality.