The FIT Framework for Mitigating Participant Fraud in Online Surveys: A Synthesis of Two International LGBTQ+ Social Research Case Studies

Participant fraud poses a growing threat to international LGBTQ+ social research, yet existing mitigations recommendations are fragmented and inconsistent across the literature. This paper addresses this gap by synthesizing insights across two case studies of international surveys on LGBTQ+ populations compromised by fraud. Case study 1 (N = 1,707) details a preventative screening process in a survey about LGBTQ+ leisure spaces, combining Qualtrics security tools with geolocation and email checks. Case study 2 (N = 3,681) outlines an eliminative strategy in an LGBTQ+ video gaming survey using 10 fraud indicators analyzed using unsupervised machine learning. Each study found fraud rates of 43-45% (n = 2,540) and fraud check utility varied across studies. Practical context-dependent recommendations and future research directions are synthesized into the feasibility, inclusion, trustworthiness (FIT) framework, emphasizing methodological and ethical trade-offs between competing research demands and the need for dynamic and tailored mitigation strategies.