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Using Epidemiological Test Diagnostics to Select Fraud Detection Methods: Secondary Analysis of Quantitative

Rachel Willard-Grace1, Tali Klima2, Mansi Dedhia1

  • 1Department of Family and Community Medicine, University of California, San Francisco, Box 1315 2540 23rd St., Floor 5, 5511, San Francisco, CA, 94143, United States, 1 415-297-3969.

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|March 11, 2026
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Bot attacks threaten survey data integrity by creating fraudulent records. This study found that most methods for detecting bot attacks have low sensitivity, but combining fraud markers and repeated response blocks is effective.

Keywords:
botshealth care surveysquestionnairessensitivityspecificitysurvey methodologysurveysweb-based data collection

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Area of Science:

  • Survey methodology
  • Computational social science
  • Epidemiology

Background:

  • Bot attacks, a form of participant fraud, compromise survey data integrity and can silence marginalized voices.
  • Scalable methods for identifying and mitigating bot attacks are crucial but limited in current research guidance.

Purpose of the Study:

  • To assess the effectiveness of recommended fraud detection methods using an epidemiological diagnostic test framework.
  • To provide web-based survey researchers with guidance on identifying and mitigating bot attacks.

Main Methods:

  • Analysis of a cross-sectional web-based survey on pediatric subspecialty care access in California.
  • Recruitment of caregivers of children with chronic conditions through family resource centers.
  • Application of an epidemiological diagnostic test framework to evaluate fraud detection strategies, including sensitivity, specificity, and predictive values.

Main Results:

  • Out of 1551 total survey records, 905 were identified as fraudulent due to bot attacks.
  • Sudden spikes in responses were the primary indicator of bot attacks.
  • Most literature-recommended methods showed low sensitivity in detecting bot attacks; combinations of fraud markers and repeated response blocks proved most effective.

Conclusions:

  • Bot attacks pose a significant and growing threat to survey research data quality.
  • Flexible, adaptable protocols are essential for effectively identifying and mitigating bot attacks.
  • Accurate survey data is vital for ensuring that the voices of real participants inform policy and programmatic decisions.