Detecting and Mitigating Fraudulent Participation
Lessons Learned from a Mixed Methods Study
Bibliographic Data
| ID | 12811588 |
|---|---|
| Authors | Michelle Lamont (0000-0003-0023-2971, University of Ottawa, corresponding author), Ligyana Korki de Candido (0000-0001-5089-4784, University of Ottawa), Delane Linkiewich (0000-0002-7678-699X, University of Guelph), Sina Negarandeh (University of Ottawa), Bruce Dick (0000-0003-0404-4927, University of Alberta), JENNIFER N STINSON (University of Toronto), Abbie Jordan (0000-0003-1595-5574, University of Bath), Verena Kantere (University of Ottawa), Rachel Kelly (0000-0002-8364-1836, Hospital for Sick Children), Paula Forgeron (0000-0002-4686-9698, University of Ottawa) |
| Year | 2026 |
| Publication date | 2026-01-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Mixed Methods Research (JOURNAL) |
| Journal identifiers | ISSN: 1558-6898 • E-ISSN: 1558-6901 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/15586898261416725 |
| OpenAlex | W7125011634 |
| Language | EN |
| Citations received | 1 |
| References cited | 33 |
A surge in social media research recruitment has led to increased fraudulent participation, impacting studies such as our sequential mixed methods research (MMR) study on peer loneliness among adolescents with chronic pain during COVID-19. The purpose of this paper is to describe the challenges and subsequent strategies implemented to prevent and identify fraudulent participants during a MMR study that used online data collection methods. The results of the various mitigation strategies implemented are provided along with recommendations for future research. This article makes a valuable contribution to MMR literature by highlighting the threat to data integrity and detailing various mitigation strategies for different phases of MMR. These strategies should be proactively implemented by researchers to increase data integrity
Data collection · Loneliness · Multimethodology · Social media · Mental Health via Writing · Mobile Crowdsensing and Crowdsourcing · Social Media in Health Education
Got Bots? Practical Recommendations to Protect Online Survey Data from Bot Attacks
Threats of Bots and Other Bad Actors to Data Quality Following Research Participant Recruitment Through Social Media
Detecting, Preventing, and Responding to “Fraudsters” in Internet Research
The Redcap consortium
Ensuring survey research data integrity in the era of internet bots
Random Responding as a Threat to the Validity of Effect Size Estimates in Correlational Research
‘Imposter participants’ in online qualitative research, a new and increasing threat to data integrity
Social media use informing behaviours related to physical activity, diet and quality of life during Covid-19
Bots and nots
Threats to Online Surveys
A quasi-experimental study examining the efficacy of multimodal bot screening tools and recommendations to preserve data integrity in online psychological research
Suspicious and fraudulent online survey participation
Dealing With Scam in Online Qualitative Research
Qualitative Data Collection in an Era of Social Distancing
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |