Evidence map›Paper›PMID 39680887›Full record

ArticleJournal of medical Internet research2024

From Doubt to Confidence-Overcoming Fraudulent Submissions by Bots and Other Takers of a Web-Based Survey.

Jeffrey J Hardesty, Elizabeth Crespi, Joshua K Sinamo, Qinghua Nian, Alison Breland, Thomas Eissenberg, Ryan David Kennedy, Joanna E Cohen

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
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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

8 authors.

Jeffrey J HardestyInstitute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-4705-4976
Elizabeth CrespiInstitute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-5462-5976
Joshua K SinamoInstitute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-3579-7390
Qinghua NianInstitute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-9551-1599
Alison BrelandCenter for the Study of Tobacco Products, Department of Psychology, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0001-8949-2395
Thomas EissenbergCenter for the Study of Tobacco Products, Department of Psychology, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0002-8277-5437
Ryan David KennedyInstitute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-9448-5234
Joanna E CohenInstitute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-3869-3637

Funding

Project 4: Using a prospective cohort survey to test population-level predictions generated by Projects 1-3U54DA036105 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI FAGAN, PEBBLES · 2018 to 2022
$20.8M
NIDA NIH HHS U54 DA036105
6 · The paper itself

Abstract

In 2019, we launched a web-based longitudinal survey of adults who frequently use e-cigarettes, called the Vaping and Patterns of E-cigarette Use Research (VAPER) Study. The initial attempt to collect survey data failed due to fraudulent survey submissions, likely submitted by survey bots and other survey takers. This paper chronicles the journey from that setback to the successful completion of 5 waves of data collection. The section "Naïve Beginnings" examines the study preparation phase, identifying the events, decisions, and assumptions that contributed to the failure (eg, allowing anonymous survey takers to submit surveys and overreliance on a third-party's proprietary fraud detection tool to identify participants attempting to submit multiple surveys). "A 5-Alarm Fire and Subsequent Investigation" summarizes the warning signs that suggested fraudulent survey submissions had compromised the data integrity after the initial survey launched (eg, an unanticipated acceleration in recruitment and a voicemail alleging fraudulent receipt of multiple gift codes). This section also covers the investigation process, along with conclusions regarding how the methodology was exploited (eg, clearing cookies and using virtual private networks) and the extent of the issue (ie, only 363/1624, 22.4% of the survey completions were likely valid). "Building More Resilient Methodology" details the vulnerabilities and threats that likely compromised the initial survey attempt (eg, anonymity and survey bots); the corresponding mitigation strategies and their benefits and limitations (eg, personal record verification platforms, IP address matching, virtual private network detection services, and CAPTCHA [Completely Automated Public Turing test to tell Computers and Humans Apart]); and the array of strategies that were implemented in future survey attempts. "Staying Vigilant" recounts the identification and management of an additional threat that emerged despite the implementation of an array of mitigation strategies, underscoring the need for ongoing vigilance and adaptability. While the precise nature of the threat remains unknown, the evidence suggested multiple fraudulent surveys were submitted by a single or connected entities, who likely did not possess e-cigarettes. To mitigate the chance of reoccurrence, participants were required to submit an authentic photo of their most used e-cigarette. Finally, in "Reflection 4 Years Later," we share insights after completing 5 waves of data collection without additional threats or vulnerabilities uncovered that necessitated the application of further mitigation strategies. Reflections include reasons for confidence in the data's integrity, the scalability and cost-effectiveness of the study protocols, and the potential introduction of sampling bias through recruitment and mitigation strategies. By sharing our journey, we aim to provide valuable insights for researchers facing similar challenges with web-based surveys and those seeking to minimize such challenges a priori. Our experiences highlight the importance of proactive measures, continuous monitoring, and adaptive problem-solving to ensure the integrity of data collected from participants recruited from web-based platforms.

Indexed as

FraudInternetAdultElectronic Nicotine Delivery SystemsHumansLongitudinal StudiesSurveys and QuestionnairesVapingadultcessationchallengesdata collectiondata integritydata qualitye-cigarettee-cigsfake datainternet surveylongitudinal surveyonline surveyprevalencerecruitmentsmokesmokingtobacco controlUnited StatesUSVAPERvapingVaping and Patterns of E-cigarette Use Researchweb-based survey

Identifiers

PMID39680887
PMCPMC11686022

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.