Flagged for Fraud: Lessons From 3 Case Studies on Detecting Inauthentic Participants in Online Research

Abstract

As digital and remote research methods become more prevalent, the risk of fraudulent participants—individuals who deliberately misrepresent themselves to gain access to studies and associated incentives—has emerged as a significant challenge. These inauthentic participants threaten data validity, obscure treatment effects, and may lead to interventions being developed based on inaccurate representations of target populations. Despite the growing recognition of this issue, researchers have limited guidance on how to detect and respond to fraud when it occurs, particularly when committed by real people rather than automated systems. We present 3 case studies from our own research where participants engaged in deception to gain study incentives. We identify recurring patterns of behavior as “red” (clear signs of inauthenticity) and “yellow” (ambiguous behavior common among fraudulent participants) flags, describe how our team responded, and share lessons learned for future studies. This work aims to support researchers in identifying fraudulent participants more effectively, helping to ensure the validity and credibility of data collected in online research.