Preventing and Eliminating Bots and Participant Fraud in Online Surveys: Two Case Studies from International LGBTQ+ Social Research
Bibliographic record
Abstract
Participant fraud from bots and ineligible participants poses a growing threat to international social research, requiring bespoke mitigation strategies. This paper presents two case studies of international LGBTQ+ surveys compromised by fraud and describes preventative and eliminative mitigation. Case study 1 (N = 1,707) describes a preventative screening process in a survey about LGBTQ+ leisure spaces, combining Qualtrics security tools with geolocation and email address checks. Case study 2 (N = 3,681) describes an eliminative strategy in an LGBTQ+ video gaming survey, using 10 fraud indicators analyzed using hierarchical and K-means cluster analysis. Both studies found fraud rates of 43-45%, illustrating the value of tailored, multi-layered strategies. Some indicators (e.g., IP address blacklisting, attention checks) were less effective, possibly due to privacy-seeking behaviours and neurodiversity in LGBTQ+ samples, but unique demographic characteristics could support sample validation. Strengths and limitations of preventative and eliminative approaches are compared, and practical recommendations offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".