Bots and Fake Participants: Ensuring Valid and Reliable Data Collection Using Online Participant Recruitment Methods
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| Title: | Bots and Fake Participants: Ensuring Valid and Reliable Data Collection Using Online Participant Recruitment Methods |
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| Language: | English |
| Authors: | Roseline Jean Louis (ORCID |
| Source: | International Journal of Social Research Methodology. 2025 28(4):463-473. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2025 |
| Sponsoring Agency: | National Institute of Nursing Research (NINR) (DHHS/NIH) |
| Contract Number: | F31NR020575 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Research Problems, Recruitment, Selection Tools, Internet, Social Media, Online Surveys, Artificial Intelligence, Deception, Credibility, Research Administration, Research, Research Projects, Medical Research |
| DOI: | 10.1080/13645579.2024.2410176 |
| ISSN: | 1364-5579 1464-5300 |
| Abstract: | Recruitment for successful health sciences research requires balancing efficiency, cost, accessibility, and reliability of available recruitment methods. Our case-control study used online recruitment methods, which broadened our reach to potential participants across the United States. However, this approach also exposed us to challenges associated with bot interference and fraudulent participation. This paper focuses on maintaining data integrity, specifically when utilizing online participant recruitment methods. Drawing from our experience, we propose The Swiss Cheese Model of Study Participant Fraud Prevention, adapted from Reason's Swiss Cheese Model, and illustrate 10 prevention and verification measures that can be taken to minimize fraud in research studies that rely on online recruitment. We emphasize the importance of a layered approach, including carefully designed recruitment media and compensation protocols, vetting of participant eligibility, and data verification protocols to ensure the validity and reliability of research findings in the digital age. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1495994 |
| Database: | ERIC |
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| Abstract: | Recruitment for successful health sciences research requires balancing efficiency, cost, accessibility, and reliability of available recruitment methods. Our case-control study used online recruitment methods, which broadened our reach to potential participants across the United States. However, this approach also exposed us to challenges associated with bot interference and fraudulent participation. This paper focuses on maintaining data integrity, specifically when utilizing online participant recruitment methods. Drawing from our experience, we propose The Swiss Cheese Model of Study Participant Fraud Prevention, adapted from Reason's Swiss Cheese Model, and illustrate 10 prevention and verification measures that can be taken to minimize fraud in research studies that rely on online recruitment. We emphasize the importance of a layered approach, including carefully designed recruitment media and compensation protocols, vetting of participant eligibility, and data verification protocols to ensure the validity and reliability of research findings in the digital age. |
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| ISSN: | 1364-5579 1464-5300 |
| DOI: | 10.1080/13645579.2024.2410176 |
