Challenges and Lessons Learned in Managing Web-Based Survey Fraud for the Garnering Effective Outreach and Research in Georgia for Impact Alliance–Community Engagement Alliance Survey Administrations
Bibliographic record
Abstract
Background: Convenience, privacy, and cost-effectiveness associated with web-based data collection have facilitated the recent expansion of web-based survey research. Importantly, however, practical benefits of web-based survey research, to scientists and participants alike, are being overshadowed by the dramatic rise in suspicious and fraudulent survey submissions. Misinformation associated with survey fraud compromises data quality and data integrity with important implications for scientific conclusions, clinical practice, and social benefit. Transparency in reporting on methods used to prevent and manage suspicious and fraudulent submissions is key to protecting the veracity of web-based survey data; yet, there is limited discussion on the use of antideception strategies during all phases of survey research to detect and eliminate low-quality and fraudulent responses. Objective: This study aims to contribute to an evolving evidence base on data integrity threats associated with web-based survey research by describing study design strategies and antideception tools used during the web-based administration of the Garnering Effective Outreach and Research in Georgia for Impact Alliance-Community Engagement Alliance (GEORGIA CEAL) Against COVID-19 Disparities project surveys. Methods: GEORGIA CEAL was established in response to the COVID-19 pandemic and the need for rapid, yet, valid, community-informed, and community-owned research to guide targeted responses to a dynamic, public health crisis. GEORGIA CEAL Surveys I (April 2021 to June 2021) and II (November 2021 to January 2022) received institutional review board approval from the Morehouse School of Medicine and adhered to the CHERRIES (Checklist for Reporting Results of Internet E-Surveys). Results: A total of 4934 and 4905 submissions were received for Surveys I and II, respectively. A small proportion of surveys (Survey I: n=1336, 27.1% and Survey II: n=1024, 20.9%) were excluded due to participant ineligibility, while larger proportions (Survey I: n=1516, 42.1%; Survey II: n=1423, 36.7%) were flagged and removed due to suspicious activity; 2082 (42.2%) and 2458 (50.1%) of GEORGIA CEAL Surveys I and II, respectively, were retained for analysis. Conclusions: Suspicious activity during GEORGIA CEAL Survey I administration prompted the inclusion of additional security tools during Survey II design and administration (eg, hidden questions, Completely Automated Public Turing Test to Tell Computers and Humans Apart verification, and security questions), which proved useful in managing and detecting fraud and resulted in a higher retention rate across survey waves. By thorough discussion of experiences, lessons learned, and future directions for web-based survey research, this study outlines challenges and best practices for designing and implementing a robust defense against survey fraud. Finally, we argue that, in addition to greater transparency and discussion, community stakeholders need to be intentionally and mindfully engaged, via approaches grounded in community-based participatory research, around the potential for research to enable scientific discoveries in order to accelerate investment in quality, legitimate survey data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.266 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".