Bot invasion: protecting the integrity of online surveys against spamming
Bibliographic record
Abstract
Abstract Despite the various advantages of online surveys, such as their cost-effectiveness and broad reach, the infiltration of bots can result in data distortion, eroding trust and hindering effective decision-making. Identifying bot responses within survey data is paramount, and epidemiologic and public health researchers can utilise various tactics such as email authentication and scrutiny of response times, to detect fraudulent responses. This paper discusses the authors’ experience of bot spamming in an online survey, which skewed our findings. We discuss the actions taken to detect and invalidate bot responses within survey data and discuss potential forms of bot prevention. To detect fraudulent responses, the authors investigated the time taken to complete the survey, recruitment rates, invalid email addresses, and invalid free-format responses. Supplementary strategies, such as data validation methods and monitoring tools, can complement reCAPTCHA systems to alleviate the adverse effects of bot activity on survey data accuracy. However, employing other methods that require challenges, or additional questions may reduce the recruitment rate and deter potential participants. Given the advancing sophistication of bots, ongoing innovation in authentication techniques is imperative to protect the dependability and accuracy of survey data in the future.
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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 | 0.067 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".