Scammer Participants and AI-Assisted Interviews in Qualitative Health Research
An Example From a Study of Expectant Parents With a Rare Genetic Condition
Datos Bibliográficos
| ID | 7156554 |
|---|---|
| Autores | Gamze Kaplan (0000-0003-4613-1359, University of Manchester, autor de correspondencia), Shruti Garg (0000-0002-4472-4583, University of Manchester), Ming Wai Wan (0000-0002-5353-786X, University of Manchester), Debbie Smith (0000-0001-7875-1582, University of Manchester), Debbie M Smith |
| Año | 2025 |
| Volumen | 24 |
| Fecha de publicación | 2025-10-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International Journal of Qualitative Methods (JOURNAL) |
| Identificadores de la revista | ISSN: 1609-4069 • E-ISSN: 1609-4069 |
| Editorial | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/16094069251390983 |
| OpenAlex | W4416324749 |
| Idioma | EN |
| Referencias citadas | 28 |
Online interviewing has allowed cost-effective data collection and access to diverse, geographically dispersed populations. However, these benefits come with significant risks to research integrity, particularly in terms of attracting potential scammer participants—individuals who volunteer to take part in research by creating a false identity that fits the eligibility criteria for a research study. This paper explores our experiences in conducting online interviews for a qualitative health research study involving expectant parents with a rare genetic condition. We outline the strategies we implemented to deter fraudulent interest and participation at different stages of recruitment and data collection and share our critical reflections on how we managed these challenges. This paper highlights how Artificial Intelligence (AI)-assisted software may help scammers to evade detection, representing a new external threat to the validity with online qualitative research. We provide practical recommendations for safeguarding qualitative research in the digital age, along with a checklist for the design stage, informed by recent technological advancements and lessons learned from our study
Checklist · Data collection · Interview · Qualitative property · Qualitative research · Safeguarding · Focus Groups and Qualitative Methods · Mobile Crowdsensing and Crowdsourcing · Social Media in Health Education
Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching
Detecting, Preventing, and Responding to “Fraudsters” in Internet Research
Pragmatic and ethical challenges in navigating inauthentic participation in remote qualitative research
Ensuring survey research data integrity in the era of internet bots
‘Imposter participants’ in online qualitative research, a new and increasing threat to data integrity
Imposter Participants in Synchronous Qualitative Research
Identifying Multiple Submissions in Internet Research
Bots and nots
More or less than human? Evaluating the role of AI-as-participant in online qualitative research
Voice-only Skype for use in researching sensitive topics
Bias From Potentially Mischievous Responders on Large-Scale Estimates of Lesbian, Gay, Bisexual, or Questioning (LGBQ)–Heterosexual Youth Health Disparities
At least in person there would have been a cup of tea’
Beyond the challenge to research integrity
From Challenge to Opportunity
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |