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This is a Cross-Sectional Study study.

October 2, 2025

Debugging the Internet Survey: A Tutorial for Detecting Fraudulent Responses

Key Points

Manual and automated detection techniques can greatly improve the integrity of data collected through surveys.
Identifying methods to detect fraud before and during data collection helps maintain research validity and trust.
The iterative process for detection outlined in the tutorial incorporates practical suggestions based on existing literature.
Proactive fraud detection minimizes long-term resource burdens and enhances overall survey quality.

Abstract

Widely distributed cross-sectional surveys remain a valuable research tool; however, they are also vulnerable to fraud in the form of “bots” (automated respondents) and humans completing a survey multiple times or when they are not eligible. Often, “infiltration” by such fraudulent responses is only noted and addressed after data collection has started. Here we present a tutorial based on the literature and our experience from a large survey of video game behaviour. We outline an iterative process consisting of automated and manual detection techniques with practical suggestions for customization and implementation in other research. By proactively considering how to detect and respond to fraud, researchers can have greater confidence in the integrity of their data, preserve limited resources, and minimize burden from post hoc fraud identification.

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