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Summary

Online survey bots generate fake responses, skewing results and increasing costs. Researchers found that response coherence, Mahalanobis distance, and person-total correlation effectively detect these nonhuman data sets.

Keywords:
BotnetFunctional methodMahalanobis distanceMechanical TurkPerson–total correlationRandom respondingResponse coherence

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Area of Science:

  • Psychometrics
  • Data Science
  • Survey Methodology

Background:

  • Online data collection, including platforms like Amazon's Mechanical Turk (MTurk), faces challenges from automated bots generating responses.
  • These bots create economic and scientific issues by inflating research costs and compromising study validity with invalid data.
  • Existing methods for detecting problematic response sets have not been rigorously tested for their ability to identify nonhuman data.

Purpose of the Study:

  • To empirically compare the effectiveness of various indices in detecting nonhuman response sets in online surveys.
  • To identify reliable methods for distinguishing between human and bot-generated survey data.
  • To provide practical tools for researchers to ensure data integrity in online studies.

Main Methods:

  • Seven different indices were evaluated for their ability to detect nonhuman response sets.
  • A dataset of 1,967 human responses was combined with simulated random response sets (5% to 50%) to mimic bot activity.
  • Indices were compared based on their performance in identifying these simulated nonhuman responses, assuming bots use random response distributions.

Main Results:

  • Three indices demonstrated superior performance in detecting nonhuman response sets: response coherence, Mahalanobis distance, and person-total correlation.
  • Mahalanobis distance and person-total correlation were identified as particularly easy to calculate.
  • These findings highlight specific, accessible methods for identifying automated survey responses.

Conclusions:

  • Response coherence, Mahalanobis distance, and person-total correlation are effective measures for detecting nonhuman survey responses.
  • The ease of calculation for Mahalanobis distance and person-total correlation makes them practical tools for researchers.
  • Implementing these indices can help researchers screen for and remove invalid data, improving the quality of online survey research.