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This paper is in the following e-collection/theme issue:

Viewpoints and Perspectives (624) Ethics, Privacy, and Legal Issues (691) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1055) mHealth for Data Collection and Research (1305) Smoking Cessation (667) Behavioural Surveillance for Public Health (325) Bots and AI Approaches to Detect and Counter Misinformation (24)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60184, first published .
Person filling out personal information form on a smartphone screen

From Doubt to Confidence—Overcoming Fraudulent Submissions by Bots and Other Takers of a Web-Based Survey

From Doubt to Confidence—Overcoming Fraudulent Submissions by Bots and Other Takers of a Web-Based Survey

Authors of this article:

Jeffrey J Hardesty1 Author Orcid Image ;   Elizabeth Crespi1 Author Orcid Image ;   Joshua K Sinamo1 Author Orcid Image ;   Qinghua Nian1 Author Orcid Image ;   Alison Breland2 Author Orcid Image ;   Thomas Eissenberg2 Author Orcid Image ;   Ryan David Kennedy1 Author Orcid Image ;   Joanna E Cohen1 Author Orcid Image
Article Authors Cited by (13) Tweetations Metrics

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  1. Liem D. The future of online or web-based research. Have you been BOTTED?. Appetite 2025;213:108058 View
  2. Caven I, Yang Z, Okrainec K. It’s raining bots: how easier access to internet surveys has created the perfect storm. BMJ Open Quality 2025;14(2):e003208 View
  3. Timothy T. AI-driven fabrication of healthcare survey data: methods, motivations, and ethical implications. Ethics & Behavior 2026;36(4):325 View
  4. Cha S, Edwards G, Jacobs M, Molloy I, Papandonatos G, Graham A. Successful Digital Recruitment of Adolescents for Population-Based Research: A Case Study of Methods and Strategies. Academic Pediatrics 2026;26(2):103181 View
  5. Bontemps A, Pridgen B, Wagner W, Cropsey K, Mitra S. A national study of substance use: Demonstrated use of recommendations for best practice online data collection. PLOS One 2025;20(11):e0336612 View
  6. Sefcik J, Boon J, Buck H, Frechman E, Wallace C, Carpenter J, Gaugler J. Gerontologists combating fraudulent research participants: recommendations from design to dissemination. The Gerontologist 2025;65(12) View
  7. Nyman A, Henderson K, Holland Jr. J, Ashley D, Spears C, Huang J, Weaver S. Cross-sectional comparison of sociodemographic and tobacco use characteristics of US adults who regularly use products of leading electronic nicotine delivery systems (ENDS). Tobacco Induced Diseases 2025;23(December):1 View
  8. Willard-Grace R, Klima T, Dedhia M, Lo E, Nisnevich A, Gray A, Henry H. Selecting fraud detection methods: Using epidemiological test diagnostics to evaluate responses in a cross-sectional questionnaire study (Preprint). Journal of Medical Internet Research 2025 View
  9. Kittle J, Brown A. Fraudulent qualitative study participants: experiences of two nurse researchers. Nurse Researcher 2026;34(2):35 View
  10. Towne K, Polivka B. Mitigating Fraud in Online Surveys: A Methodological Approach. Western Journal of Nursing Research 2026;48(6):661 View
  11. LoGiudice J, Sperlich M, Kalu O, Seng J. Mixed‐Methods Study of the Impact of Interpersonal Trauma on Midwives’ Professional Practice: “Of Course It Affects My Work”. Journal of Midwifery & Women's Health 2026 View
  12. McDuff K, Su T, Brown D, Martin J, Chan Carusone S, O'Connell S, O'Donovan I, St. Clair-Sullivan N, Townsend L, Goulding S, Kelly M, McCorkell L, Wei H, O'Hara M, Soares L, Avery L, Bannan C, Bergin C, Harding R, Nathanson J, Solomon P, Cheung A, Vera J, O'Brien K. Battling the bots: Defending against fraudulent responses while conducting an international community-engaged web-based survey with people living with Long COVID (Preprint). Journal of Medical Internet Research 2025 View
  13. Ruger A, Abdalazeem M, Christofa E, Oke J. Explainable Data-Driven Multi-filter Framework for Bot Detection in Incentivized Online Surveys. Data Science for Transportation 2026;8(2) View