Evidence map›Paper›PMID 42695531›Full record

ArticleEthics & human research

Digital Deception: Survey Bots and the Ethics of Data Integrity.

Whanyichukwu Frank-Ito, Lori Ann Eldridge, Jennifer Matthews

Abstract read
In one paragraph

Article in Ethics & human research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Whanyichukwu Frank-ItoStudent in the Department of Health and Human Performance at East Carolina University.
Lori Ann EldridgeAssistant professor in the Department of Health and Human Performance at East Carolina University.
Jennifer MatthewsProfessor in the Department of Health and Human Performance at East Carolina University.

Funding

Centers for Disease Control and Prevention (CDC) NH28CE003320
6 · The paper itself

Abstract

The rapid growth of artificial intelligence and automated systems has significantly impacted digital survey-based research, particularly when recruitment is conducted via social media platforms. This case study details the erosion of data integrity in a 2025 follow-up survey, originally designed to replicate a successful 2021 study. Despite implementing increasingly sophisticated antifraud mechanisms such as CAPTCHA, IP geo-restrictions, and behavioral validation, survey bots repeatedly infiltrated the study, rendering the dataset unusable. This highlights a critical tension between broad digital accessibility and the vulnerability of online research to fraud. Moreover, the use of financial incentives to participate in research, while effective in increasing response rates, was found to amplify bot activity and raise ethical dilemmas surrounding compensation and data quality. As automated deception continues to evolve, researchers must navigate a complex landscape where conventional protections are no longer sufficient. The authors call for the urgent development of fieldwide standards, institutional support, and robust fraud prevention strategies to preserve the methodological and ethical integrity of digital research. Without such measures, the viability of online recruitment and the trustworthiness of the resulting data remain at serious risk.

Indexed as

Artificial IntelligenceDeceptionSocial MediaDigital MediaEthical DilemmasEthics, ResearchFraudHumansSurveys and Questionnairesartificial intelligencedata integrityethical researchfinancial incentivefraud mitigationhuman research ethicsonline recruitmentsurvey bots

Identifiers

PMID42695531
PMCPMC13543757

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.